Context-aware recommender systems better identify interesting items for users by adapting their suggestions to the specific contextual situations, e.g., to the current weather, if an excursion is to be recommended . But, the cold-start problem may jeopardise the quality of the recommendations: for users, items or contextual situations that are new to the system, recommendations are hard to compute. We have developed a number of novel techniques to tame this problem, and in particular, new hybrid algorithms that combine several, simpler, algorithms in order to exploit their strengths and avoid their weaknesses. We have also developed algorithms for actively identifying the most useful preference information to ask the user in order to bootstrap the system. Our results obtained from a series of offline and online experiments reveal that the proposed techniques can effectively alleviate the cold-start problem of context-aware recommender systems.
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
Artwork Personalization at Netflix Fernando Amat RecSys2018 Fernando Amat
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
Artwork Personalization at Netflix Fernando Amat RecSys2018 Fernando Amat
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Netflix talk at ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Fernando Amat and Elliot Chow from Netflix talk about the Bandit infrastructure for Personalized Recommendations
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + FugueDatabricks
Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution.
But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why?
Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models?
Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case?
Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark?
Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize?
In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints
Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna
It firstly models the general tuning problems, independent from machine learning
It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem
It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark.
In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
At Netflix, we try to provide the best personalized video recommendations to our members. To do this, we need to adapt our recommendations for each contextual situation, which depends on information such as time or device. In this talk, I will describe how state of the art Contextual Recommendations are used at Netflix. A first example of contextual adaptation is the model that powers the Continue Watching row. It uses a feature-based approach with a carefully constructed training set to learn how to adapt to the context of the member. Next, I will dive into more modern approaches such as Tensor Factorization and LSTMs and share some results from deployments of these methods. I will highlight lessons learned and some common pitfalls of using these powerful methods in industrial scale systems. Finally, I will touch upon system reliability, choice of optimization metrics, hidden costs, risks and benefits of using highly adaptive systems.
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Recap: Designing a more Efficient Estimator for Off-policy Evaluation in Band...Justin Basilico
Talk from the REVEAL workshop at RecSys 2019 on 2019-09-20 in Copenhagen, Denmark. The slides were primarily made by Ajinkya More and the paper was also joint work with Linas Baltrunas and Nikos Vlassis.
The paper is available here: https://drive.google.com/open?id=1oaM5Fu2bJ0GzMC09yyqjA7eZD9axzSKb
Presented at the Machine Learning class at Chalmers, Gothenburg.
http://www.cse.chalmers.se/research/lab/courses.php?coid=9
Trying to connect their theoretical machine learning class with industry examples.
Building Data Pipelines for Music Recommendations at SpotifyVidhya Murali
In this talk, we will get into the architectural and functional details as to how we build scalable and robust data pipelines for music recommendations at Spotify. We will also discuss some of the challenges and an overview of work to address these challenges.
This introductory lecture for IA377 will be devoted to the topic of “Literature Review”.
What is a literature review?
Methodology, best practices, tips, tools, etc.
Practical example
Application to IA377 seminar activities.
https://ia377-feec-unicamp.github.io/classes/2023/03/09/Literature-Review.html
Netflix talk at ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Fernando Amat and Elliot Chow from Netflix talk about the Bandit infrastructure for Personalized Recommendations
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + FugueDatabricks
Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution.
But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why?
Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models?
Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case?
Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark?
Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize?
In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints
Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna
It firstly models the general tuning problems, independent from machine learning
It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem
It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark.
In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
At Netflix, we try to provide the best personalized video recommendations to our members. To do this, we need to adapt our recommendations for each contextual situation, which depends on information such as time or device. In this talk, I will describe how state of the art Contextual Recommendations are used at Netflix. A first example of contextual adaptation is the model that powers the Continue Watching row. It uses a feature-based approach with a carefully constructed training set to learn how to adapt to the context of the member. Next, I will dive into more modern approaches such as Tensor Factorization and LSTMs and share some results from deployments of these methods. I will highlight lessons learned and some common pitfalls of using these powerful methods in industrial scale systems. Finally, I will touch upon system reliability, choice of optimization metrics, hidden costs, risks and benefits of using highly adaptive systems.
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Recap: Designing a more Efficient Estimator for Off-policy Evaluation in Band...Justin Basilico
Talk from the REVEAL workshop at RecSys 2019 on 2019-09-20 in Copenhagen, Denmark. The slides were primarily made by Ajinkya More and the paper was also joint work with Linas Baltrunas and Nikos Vlassis.
The paper is available here: https://drive.google.com/open?id=1oaM5Fu2bJ0GzMC09yyqjA7eZD9axzSKb
Presented at the Machine Learning class at Chalmers, Gothenburg.
http://www.cse.chalmers.se/research/lab/courses.php?coid=9
Trying to connect their theoretical machine learning class with industry examples.
Building Data Pipelines for Music Recommendations at SpotifyVidhya Murali
In this talk, we will get into the architectural and functional details as to how we build scalable and robust data pipelines for music recommendations at Spotify. We will also discuss some of the challenges and an overview of work to address these challenges.
This introductory lecture for IA377 will be devoted to the topic of “Literature Review”.
What is a literature review?
Methodology, best practices, tips, tools, etc.
Practical example
Application to IA377 seminar activities.
https://ia377-feec-unicamp.github.io/classes/2023/03/09/Literature-Review.html
Module 3 - CaseMethodology and FindingsCase AssignmentThe Ca.docxaudeleypearl
Module 3 - Case
Methodology and Findings
Case Assignment
The Case Assignments in this course are designed to assist you with the completion of the Doctoral Study Proposal. Each module will provide you with instructions and guidance on how to complete a component of the proposal. You are expected to follow the steps below:
· Review all module content, including the information provided on the module homepage
· Incorporate any changes into your Case 3 assignment based on instructor feedback from Case 2
· Use the track changes function in Word, so the instructor can follow the modifications you make to your document based on Case 2 feedback
Using the module content as a guide, draft the following sections:
First, incorporate the feedback received on your Module 2 Case 2 assignment and update the following sections to include those changes in your Case 3 assignment:
Background
Statement of the Problem
Purpose of the Study
Conceptual or Theoretical Framework
Research Design
Significance of the Study
Next, draft the following sections:
Research Methods and Design
Research Site or Population
Population and Sample
Instrumentation
Section 3: Methodology and Findings
Research Methods and Design
Describe your overall research approach. Discuss why qualitative, quantitative, or mixed methods have been selected to address your topic. Discuss the selected research design and justification for the selection of the design for your study.
Provide detail on your research design. Justify why the selected design is appropriate for the study.
Qualitative Research Designs
· Case Study: the school, program, job, etc. is the unit of analysis. May use interviews, observation, document analysis.
· Ethnographic/Qualitative Interview Study: the individual is the unit of analysis, 1:1 or focus group interviews are used
· Ethnography: the culture is the unit of analysis; observation, interviews and artifact collection (documents) are used.
· Narrative Study (or its pre-mutations): the story is the unit of analysis. Several individuals are interviewed in depth.
· Grounded Theory: variables needed to develop the theory are the unit of analysis; many 1:1 interviews are used.
· Phenomenological: the phenomena is the unit of analysis; many 1:1 interviews are used.
Quantitative Research Designs
· Experimental Research: To establish a possible “cause-and-effect” relationship between variables
· Types of experimental designs
· True experimental designs
· Quasi-experimental designs
· Pre-experimental designs
· Factorial designs
· Non-Experimental Research: To describe an existing condition
· Types of descriptive research
· Correlational research: to determine relationships between variables
· Causal-comparative research (aka ex post facto): to determine the “cause” for preexisting differences
· Survey research: to describe the attitudes, opinions, behaviors, or characteristics of the population
· Cross-sectional survey designs
· Longitudinal survey designs
Research Hypotheses.
Module 3 - CaseMethodology and FindingsCase AssignmentThe Ca.docxroushhsiu
Module 3 - Case
Methodology and Findings
Case Assignment
The Case Assignments in this course are designed to assist you with the completion of the Doctoral Study Proposal. Each module will provide you with instructions and guidance on how to complete a component of the proposal. You are expected to follow the steps below:
· Review all module content, including the information provided on the module homepage
· Incorporate any changes into your Case 3 assignment based on instructor feedback from Case 2
· Use the track changes function in Word, so the instructor can follow the modifications you make to your document based on Case 2 feedback
Using the module content as a guide, draft the following sections:
First, incorporate the feedback received on your Module 2 Case 2 assignment and update the following sections to include those changes in your Case 3 assignment:
Background
Statement of the Problem
Purpose of the Study
Conceptual or Theoretical Framework
Research Design
Significance of the Study
Next, draft the following sections:
Research Methods and Design
Research Site or Population
Population and Sample
Instrumentation
Section 3: Methodology and Findings
Research Methods and Design
Describe your overall research approach. Discuss why qualitative, quantitative, or mixed methods have been selected to address your topic. Discuss the selected research design and justification for the selection of the design for your study.
Provide detail on your research design. Justify why the selected design is appropriate for the study.
Qualitative Research Designs
· Case Study: the school, program, job, etc. is the unit of analysis. May use interviews, observation, document analysis.
· Ethnographic/Qualitative Interview Study: the individual is the unit of analysis, 1:1 or focus group interviews are used
· Ethnography: the culture is the unit of analysis; observation, interviews and artifact collection (documents) are used.
· Narrative Study (or its pre-mutations): the story is the unit of analysis. Several individuals are interviewed in depth.
· Grounded Theory: variables needed to develop the theory are the unit of analysis; many 1:1 interviews are used.
· Phenomenological: the phenomena is the unit of analysis; many 1:1 interviews are used.
Quantitative Research Designs
· Experimental Research: To establish a possible “cause-and-effect” relationship between variables
· Types of experimental designs
· True experimental designs
· Quasi-experimental designs
· Pre-experimental designs
· Factorial designs
· Non-Experimental Research: To describe an existing condition
· Types of descriptive research
· Correlational research: to determine relationships between variables
· Causal-comparative research (aka ex post facto): to determine the “cause” for preexisting differences
· Survey research: to describe the attitudes, opinions, behaviors, or characteristics of the population
· Cross-sectional survey designs
· Longitudinal survey designs
Research Hypotheses ...
Course Code EDU7702-8Course Start Date 02152016Sec.docxvanesaburnand
Course Code: EDU7702-8
Course Start Date: 02/15/2016
Section: Synthesis: Research problem, method, design
Week: 7
Activity: Develop Research Methodology for Hypothetical Research Study
Activity Due Date: 04/03/2016
Activity Description
For Week 6, you developed the research problem, purpose, and questions for both a qualitative and a quantitative research study. For this task, choose one of the research problems and questions that
you developed in Week 6 (either the qualitative or the quantitative) and develop the methodology for the chosen study.
Then, next week you will develop the methodology for the second study and then combine the methodology section with other elements of the study to create a concept paper. (Thus, you may want to
choose the study of most interest to you and develop the methodology for that study as part of the assignment for Week 8).
There are several documents in the NCU dissertation center that will be helpful in developing the research methodology for your Week 7 and Week 8 assignments. These include the concept paper
templates and the proposal templates. Details regarding the research methods for the dissertation are explained in Chapter 3 of the dissertation proposal. The dissertation proposal template shows the
sections that should be included in Chapter 3 of the dissertation proposal. These sections include the following:
1. Research Methods and Design(s)
2. Population
3. Sample
4. Materials/Instruments
5. Operational Definitions of Variables (Quantitative/Mixed Studies Only)
6. Data Collection, Processing, and Analysis
7. Assumptions
8. Limitations
9. Delimitations
10. Ethical Assurances
11. Summary
In developing the methodology section for this week’s assignment, you will want to address Sections 1-6 and Section 10. You will find a discussion of these sections below that will help you develop
these sections of the research methodology.
(1) Research Methods and Design: Explain the methodology and design that you will use to address the research purpose and questions. Will you use the qualitative methodology or the quantitative
methodology? Explain your reasoning for the methodology that you will use to answer the research questions. Why is the specific methodology appropriate for answering the research questions? Which
of the designs is appropriate for your study? Refer to Section 5 for a review of the qualitative and quantitative designs. Then, explain the design that you will use. When is this design appropriate for use
and why is the design appropriate for your research purpose and questions? You will want to cite sources for your reasoning to use the methodology that you use. Be sure to explain why the
methodology and design is appropriate for your study.
Potential sources for defending the methodology and design include the following:
Cozby, P. & Bates, S. (2012). Methods in behavioral research. Boston, MA: McGraw Hill Higher Education.
Creswell, J. W. (2014). Research design: Qu.
This section provides general guidance related to the research typ.docxjuliennehar
This section provides general guidance related to the research type and methodology. Please review this information carefully. There are specific research types and methods associated with the degree plan you are pursuing.
Research Study Type
For DBA students taking BUSI 987 – 990, the Research Study type is a Dissertation For DBA students taking BUSI 887 – 890, the Research Study type can be either a Case Study Project or a Consulting Project
With the dissertation or case study project approach, the student begins by researching the literature to find a problem, develops a research proposal to study the problem, and then finds an organization within which to study the problem. With the consulting project approach, the student begins with an organization with a problem, researches the literature to better understand the problem, and then develops a proposed solution to the problem. In all three cases a problem statement based upon the current literature must be developed.
Methodology
The methodology is how you will study the problem at a very high level, all research will be conducted using one of the following research approaches:
Fixed Design using Quantitative Methods Flexible Design using Qualitative Methods Mixed Method Design using Quantitative and Qualitative Methods
Fixed Designs are fully defined (fixed) as part of the research proposal and following the proposal, the researcher executes the research and analysis using quantitative tools as described. Flexible designs on the other hand are defined in a general sense as part of the research proposal and following the proposal, the researcher is free to execute adjust (flexible) the research as is necessary using qualitative tools as described. Mixed Method Designs as the name implies, use a combination of both. The choice of research approach is guided by the research questions.
Within each research approach there are specific methods that can be employed. The table below lists the most common methods utilized in the three research approaches. Once selected, the method becomes the methodology or specific ‘research design’ for the study.
Fixed Designs Experimental Quasi-experimental Nonexperimental Descriptive Correlational Causal-Comparative
Flexible Designs Narrative Phenomenology Grounded Theory Case Study Single Case Study Multiple Case Study Ethnography
Mixed Method Designs Convergent parallel Explanatory Sequential Exploratory Sequential Transformative
Common Methods for fixed, flexible and mixed research designs.
An effective way to begin the discussion of methodology is to start with the sentence: “This study will be conducted with a XXXXX design using XXXXX method(s) specifically, a XXXXX design will be used”
Examples:
This study will be conducted with a flexible design using qualitative methods specifically, a single case study design will be used.
- Or -
This study will be conducted with a fixed ...
Running head: RESEARCH TYPES
1
Title of PaperStudent NameWalden University
Class Number, Section Number, Class Name
Date of Submission
SEE PAGE 5
Title of Paper
Introduction to topic that gives the audience and idea of what you will be discussing in the paper. This should be a brief paragraph that provides an overview of the key points that will be addressed. This section should be concluded with a purpose statement. The purpose of this paper is …consider the intent of the application and list all requirements.
Research Methodologies
Discuss the attributes of quantitative and qualitative research methods and compare/contrast the type of information you can obtain from both types of research. Make sure you are referencing the course learning materials as well as some external references. You should have a minimum of three course learning resource references and two credible external references. Remember that web sites are only considered credible if they end in .gov, .edu, or .org.
Advantages and Disadvantages
Discuss the reality that there are advantages and disadvantages to both types of research.
Quantitative Research
Evaluate the advantages and disadvantages of quantitative research. When is it helpful and when is it not helpful. Consider things like type of information that you are seeking, ethics, time needed to complete, etc.
Qualitative Research
Evaluate the advantages and disadvantages of quantitative research. When is it helpful and when is it not helpful. Consider things like type of information that you are seeking, ethics, time needed to complete, etc. Also, make sure you address the argument that qualitative research is not real science. Is this true? Why or why not? What value does qualitative research have in nursing practice?
Summary
Write a one paragraph summary of the main points of the paper. This is not an area for adding new information. That should be in the body of your paper. Do not forget to appropriately cite in references in this section too. This is a good place to pull in your course learning resources again.
References
Last name, X. (Year of publication). Name of online article. Source. Retrieved from http:// www.nameofwebsite.com
Last name, X. X. (Year of publication). Name of book here. City, State Initial: Publisher.
Last name, X. X. (Year of publication). Name of journal article: Capitalize only letters after punctuation marks. The Journal of Whatever, Volume (Number), Page-Page. doi: number if available.
Last name, X. X. (Year of publication). Name of journal article: Capitalize only letters after punctuation marks. The Journal of Whatever, Volume (Number), Page-Page. doi: number if available.
Last name, X. X. (Year of publication). Name of journal article: Capitalize only letters after punctuation marks. The Journal of Whatever, Volume (Number), Page-Page. doi: number if available.
Last name, X. X. (Year of publication) ...
Nr 505 Education Specialist -snaptutorial.comDavisMurphyC68
For more classes visit
www.snaptutorial.com
NR 505 Analysis and Application of Clinical Practice
Analysis and Application of Clinical
Practice Guidelines & Scoring Rubric
Purpose
The purpose of this assignment is to provide an opportunity for students to apply and disseminate information based on practice summaries. The most common type of practice summary in healthcare is the clinical practice guideline (CPG).
Framework for Program Development and EvaluationReference.docxhanneloremccaffery
Framework for Program Development and Evaluation
Reference: Comeau, J. (2011). Framework for program development and evaluation.Unpublished, Capella University, Minneapolis, MN.
L i c e n s e d u n d e r a C r e a t i v e C o m m o n s A t t r i b u t i o n 3 . 0 L i c e n s e .
1. Understand and analyze qualitative program evaluation design.
2. Compare and contrast experimental and quasi-experimental designs.
3. Analyze pretest-posttest designs.
4. Communicate through writing that is concise, balanced, and logically organized.
Unit 3 - Program Evaluation: Qualitative Research Design
INTRODUCTION
This unit focuses on qualitative evaluation design, data collection methods, and evaluating program
effectiveness. Additionally, you will apply this knowledge to a real-world program evaluation.
OBJECTIVES
To successfully complete this learning unit, you will be expected to:
U03S1] Studies - Multimedia and Readings (Complete the following):
• Framework for Program Development and Evaluation view the flow chart/transcript
• Writing an Action Research Dissertation: Part One view the media/transcript
• Writing an Action Research Dissertation: Part Two view the media/transcript
The Writing an Action Research Dissertation media pieces will help you to understand the
academic writing standards for your doctoral program. You are expected to be proficient in this
type of writing by the end of your program. By using the advice and guidance of the media, you can
refine your academic writing and improve your success in this course and throughout your
program.
• Read Chapter 5 - Program Evaluation and Performance Measurement text
o Pay attention to question 7 on page 221. The content this question addresses will be
releant for the first discussion in this unit.
• Read Moore and Tananis's 2009 article, "Measuring Change in a Short-Term
Educational Program Using a Retrospective Pretest Design," from American Journal of
Evaluation, volume 30, issue 2, pages 189–202.
o Pay attention to the research design and data collection methods in this study. You
will be analyzing them for two upcoming assignments, one in this unit and the
other in Unit 5.
Constance
Highlight
Constance
Highlight
Constance
Highlight
Constance
Highlight
[U03A1] Unit 3 Assignment 1 - Program Evaluation: Analysis of Study Design
Using what you have learned through the readings and discussions up to this point in the course, read and analyze the 2009
journal article "Measuring Change in a Short-Term Educational Program Using a Retrospective Pretest Design" by Moore
and Tananis. After you have finished your reading of the article, formalize your analysis by addressing the following:
• Identify the research design that was employed in the Moore and Tananis study.
• Explain whether the research design is experimental or quasi-experimental. Support your explanation by
comparing and contrasting characteristics between the two types of designs.
◦ Make sure ...
Contextual Information Elicitation in Travel Recommender SystemsMatthias Braunhofer
Context-Aware Recommender Systems are advisory applications that exploit users’ preference knowledge contained in datasets of context-dependent user ratings, i.e., ratings augmented with the description of the contextual situation detected when the user experienced the item and rated it. Since the space of context-dependent ratings increases exponentially in size with the number of contextual factors, and because certain contextual information is still hard to acquire automatically (e.g., the user’s mood or the travellers’ group composition), it is fundamental to identify and acquire only those factors that truly influence the user preferences and consequently the ratings and the recommendations. In this paper, we propose a novel method that estimates the impact of a contextual factor on rating predictions and adaptively elicits from the users only the relevant ones. Our experimental evaluation, on two travel-related datasets, shows that our method compares favorably to other state-of-the-art context selection methods.
In this presentation we present a novel context-aware mobile recommender system for places of interest (POIs). Unlike existing systems, which learn users' preferences solely from their past ratings, it considers also their personality - using the Five Factor Model. Personality is acquired by asking users to complete a brief and entertaining questionnaire as part of the registration process, and is then exploited in: (1) an active learning module that actively acquires ratings-in-context for POIs that users are likely to have experienced, hence reducing the stress and annoyance to rate (or skip rating) items that the users don’t know; and (2) in the recommendation model that builds up on matrix factorization and therefore can be trained even if the users haven’t rated any items yet.
Parsimonious and Adaptive Contextual Information Acquisition in Recommender S...Matthias Braunhofer
Context-Aware Recommender System (CARS) models are trained on datasets of context-dependent user preferences (ratings and context information). Since the number of context-dependent preferences increases exponentially with the number of contextual factors, and certain contextual in- formation is still hard to acquire automatically (e.g., the user’s mood or for whom the user is buying the searched item) it is fundamental to identify and acquire those factors that truly influence the user preferences and the ratings. In particular, this ensures that (i) the user effort in specifying contextual information is kept to a minimum, and (ii) the system’s performance is not negatively impacted by irrelevant contextual information. In this paper, we propose a novel method which, unlike existing ones, directly estimates the impact of context on rating predictions and adaptively identifies the contextual factors that are deemed to be useful to be elicited from the users. Our experimental evaluation shows that it compares favourably to various state-of-the-art context selection methods.
In this presentation we illustrate a novel context-aware mobile recommender system for places of interest (POIs). Unlike existing systems, which learn users’ preferences solely from their past ratings, it considers also their personality - using the Five Factor Model. Personality is acquired by asking users to complete a brief and entertaining questionnaire as part of the registration process, and is then exploited in: (1) an active learning module that actively acquires ratings-in-context for POIs that users are likely to have experienced, hence reducing the stress and annoyance to rate (or skip rating) items that the users don’t know; and (2) in the recommendation model that builds up on matrix factorization and therefore can be trained even if the users haven’t rated any items yet.
Hybridisation Techniques for Cold-Starting Context-Aware Recommender SystemsMatthias Braunhofer
Context-Aware Recommender Systems (CARSs) suffer from the cold-start problem, i.e., the inability to provide accurate recommendations for new users, items or contextual situations. In this research, we aim at solving this problem by exploiting various hybridisation techniques, from simple heuristic-based solutions to complex adaptive solutions, in order to take advantage of the strengths of different CARS algorithms while avoiding their weaknesses in a given (cold-start) situation. Our initial research based on offline experiments using various contextually-tagged rating datasets has shown that basic CARS algorithms perform very differently in different recommendation scenarios, and that they can be effectively hybridised to achieve an overall optimal performance. Further research is now required to find the optimal method for hybridisation.
Usability Assessment of a Context-Aware and Personality-Based Mobile Recommen...Matthias Braunhofer
In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender system for places of interest (POIs) that integrates some innovative components, including: a personality questionnaire, i.e., a brief and entertaining questionnaire used by the system to learn users’ personality; an active learning module that acquires ratings-in-context for POIs that users are likely to have experienced; and a matrix factorization based recommendation module that leverages the personality information and several contextual factors in order to generate more relevant recommendations.
Adopting a system oriented perspective, we describe the assessment of the combination of the implemented components. We focus on usability aspects and report the end-user assessment of STS. It was obtained from a controlled live user study as well as from the log data produced by a larger sample of users that have freely downloaded and tried STS through Google Play Store. The result of the assessment showed that the overall usability of the system falls between “good” and “excellent”, it helped us to identify potential problems and it provided valuable indications for future system improvement.
Context-Aware Points of Interest Suggestion with Dynamic Weather Data ManagementMatthias Braunhofer
Weather plays an important role in tourists’ decision-making and, for instance, some places or activities must not be even suggested under dangerous weather conditions. In this paper we present a context-aware recommender system, named STS, that computes recommendations suited for the weather conditions at the recommended places of interest (POI) by exploiting a novel model-based context-aware recommendation technique. In a live user study we have compared the performance of the system with a variant that does not exploit weather data when generating recommendations. The results of our experiment have shown that the proposed approach obtains a higher perceived recommendation quality and choice satisfaction.
Cold-Start Management with Cross-Domain Collaborative Filtering and TagsMatthias Braunhofer
Recommender systems suffer from the new user problem, i.e., the difficulty to make accurate predictions for users that have rated only few items. Moreover, they usually compute recommendations for items just in one domain, such as movies, music, or books. In this paper we deal with such a cold-start situation exploiting cross-domain recommendation techniques, i.e., we suggest items to a user in one target domain by using ratings of other users in a, completely disjoint, auxiliary domain. We present three rating prediction models that make use of information about how users tag items in an auxiliary domain, and how these tags correlate with the ratings to improve the rating prediction task in a different target domain. We show that the proposed techniques can effectively deal with the considered cold-start situation, given that the tags used in the two domains overlap.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
Techniques for Context-Aware and Cold-Start Recommendations
1. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Techniques for Context-Aware and
Cold-Start Recommendations
Matthias Braunhofer
Supervisor: Prof. Francesco Ricci
Free University of Bozen - Bolzano
Piazza Domenicani 3, 39100 Bolzano, Italy
mbraunhofer@unibz.it
2. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
2
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
3. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
2
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
4. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Recommender Systems (RSs) are information filtering and decision
support tools suggesting interesting items to the user based on feedback
• Explicit feedback (e.g., ratings) vs. implicit feedback (e.g., browsing history)
• Two popular approaches:
• Collaborative Filtering (CF)
• Content-based
Recommender Systems
3
5. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Context is Essential
• Main idea: users can experience the same item differently depending on the
current contextual situation (e.g., weather, season, mood)
• RSs must take into account this information to deliver more useful (perceived)
recommendations
4
6. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Context-Aware Recommender Systems
• Context-Aware Recommender Systems (CARSs) improve traditional RSs
by adapting their suggestions to the contextual situations of the user and
the recommended items
• Example: Google Now
5
7. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Cold-Start Problem
• CARSs suffer from the cold-start problem
• New user problem: How do you recommend to a new user?
• New item problem: How do you recommend a new item with no ratings?
• New context problem: How do you recommend in a new context?
6
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4 5 4
? 3 5
8. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Cold-Start Problem
• CARSs suffer from the cold-start problem
• New user problem: How do you recommend to a new user?
• New item problem: How do you recommend a new item with no ratings?
• New context problem: How do you recommend in a new context?
6
1 ? 1 ?
2 5 ?
? 3 ?
3 ? 5 ?
2 5 ?
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5 ? 5 ?
4 5 4 ?
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Focus of this research
9. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
7
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
10. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
8
11. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
8
12. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
Active learning
(Elahi et al., 2013)
8
13. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
Active learning
(Elahi et al., 2013)
Cross-domain rec.
(Enrich et al., 2013)
8
14. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
Active learning
(Elahi et al., 2013)
Cross-domain rec.
(Enrich et al., 2013)
Implicit feedback
(Koren, 2008)
8
15. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
Active learning
(Elahi et al., 2013)
Cross-domain rec.
(Enrich et al., 2013)
User / item attributes
(Musto et al., 2013)
Implicit feedback
(Koren, 2008)
8
16. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
Active learning
(Elahi et al., 2013)
Cross-domain rec.
(Enrich et al., 2013)
User / item attributes
(Musto et al., 2013)
Selective context acquisition
(Baltrunas et al., 2012)
Implicit feedback
(Koren, 2008)
8
17. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional knowledge
sources
… better using existing
knowledge
Active learning
(Elahi et al., 2013)
Cross-domain rec.
(Enrich et al., 2013)
User / item attributes
(Musto et al., 2013)
Selective context acquisition
(Baltrunas et al., 2012)
Context hierarchy / similarity
(Codina et al., 2013)
Implicit feedback
(Koren, 2008)
8
18. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
9
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
19. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Welcome screen
20. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Registration screen
21. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Personality questionnaire
22. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Questionnaire results
23. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Slide-out navigation menu
24. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Suggestions screen
25. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Active learning
26. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Details screen
27. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Routing screen
28. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Profile page
29. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
11
30. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
• Personality is useful to elicit more ratings from new users than some state-of-
the-art AL strategies based on heuristics
11
31. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
• Personality is useful to elicit more ratings from new users than some state-of-
the-art AL strategies based on heuristics
• Personality can be exploited for eliciting ratings from new users that lead to
an improved system prediction accuracy
11
32. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
• Personality is useful to elicit more ratings from new users than some state-of-
the-art AL strategies based on heuristics
• Personality can be exploited for eliciting ratings from new users that lead to
an improved system prediction accuracy
• Personality can be helpful to acquire ratings from new users which result in
recommendations better tailored to the user’s context
11
33. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
12
34. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
• Hybrid CARS algorithms are beneficial for delivering accurate context-aware
rating predictions in cold-start situations
12
35. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
• Hybrid CARS algorithms are beneficial for delivering accurate context-aware
rating predictions in cold-start situations
• Hybrid CARS algorithms can achieve a high recommendation ranking quality
in cold-start situations
12
36. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
• Hybrid CARS algorithms are beneficial for delivering accurate context-aware
rating predictions in cold-start situations
• Hybrid CARS algorithms can achieve a high recommendation ranking quality
in cold-start situations
• Parsimonious and adaptive context acquisition can save time and effort of the
user by effectively identifying what contextual factors to acquire upon rating
an item
12
37. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
13
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
38. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid Context-Aware Recommenders
14
• Conjecture: it is possible to adaptively combine multiple CARS algorithms in
order to take advantage of their strengths and alleviate their drawbacks in
different cold-start situations
• Example:
(user, item,
context) tuple
CARS 1
CARS 2
Hybridization Final score
Score
Score
Hybrid CARS
39. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; items similar to the
target user in this latent space are recommended
Matrix Factorization Methods
15
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
z=
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qi
Tpu
40. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; items similar to the
target user in this latent space are recommended
Matrix Factorization Methods
15
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
z=
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qi
TpuRating prediction
41. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; items similar to the
target user in this latent space are recommended
Matrix Factorization Methods
15
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
z=
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qi
Tpu
Item preference factor
vector
42. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; items similar to the
target user in this latent space are recommended
Matrix Factorization Methods
15
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
z=
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qi
Tpu User preference factor
vector
43. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
16
ˆruic1...ck
= qi
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
44. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
16
ˆruic1...ck
= qi
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
45. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
16
ˆruic1...ck
= qi
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
46. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
16
ˆruic1...ck
= qi
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
47. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
16
ˆruic1...ck
= qi
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
48. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
SPF (Codina et al., 2013)
17
• SPF (Semantic Pre-Filtering) is a contextual pre-filtering method that, given
a target contextual situation, uses a standard MF model learnt from all the
ratings tagged with contextual situations identical or similar to the target one
• Conjecture: learning the prediction model on a larger number of ratings, even
if not obtained exactly in the target context, will help
• Key step: similarity calculation
1 -0.5 2 1
-2 0.5 -2 -1.5
-2 0.5 -1 -1
Condition-to-item co-occurrence matrix
1 -0.96 -0.84
-0.96 1 0.96
-0.84 0.96 1
Cosine similarity between conditions
49. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Content-based CAMF-CC
18
• It is a novel variant of CAMF-CC that incorporates additional sources of
information about the items, e.g., category or genre information
• Conjecture: alleviates the new item problem of CAMF-CC
ˆruic1...ck
= (qi + xa )
a∈A(i)
∑
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
A(i) set of item attributes
xa latent factor vector of item attribute a
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
50. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Content-based CAMF-CC
18
• It is a novel variant of CAMF-CC that incorporates additional sources of
information about the items, e.g., category or genre information
• Conjecture: alleviates the new item problem of CAMF-CC
ˆruic1...ck
= (qi + xa )
a∈A(i)
∑
T
pu + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
A(i) set of item attributes
xa latent factor vector of item attribute a
pu latent factor vector of user u
average rating for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
51. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Demographics-based CAMF-CC
19
• It is a novel variant of CAMF-CC that profiles users through known user
attributes (e.g., age group, gender, personality traits)
• Conjecture: alleviates the new user problem of CAMF-CC
ˆruic1...ck
= qi
T
(pu + ya )
a∈A(u)
∑ + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user attributes
ya latent factor vector of user attribute a
overall average rating
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
52. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Demographics-based CAMF-CC
19
• It is a novel variant of CAMF-CC that profiles users through known user
attributes (e.g., age group, gender, personality traits)
• Conjecture: alleviates the new user problem of CAMF-CC
ˆruic1...ck
= qi
T
(pu + ya )
a∈A(u)
∑ + ri + bu + btcj
j=1
k
∑
t∈T (i)
∑
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user attributes
ya latent factor vector of user attribute a
overall average rating
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj
ri
53. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between a set of basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles specific cold-start situations found in CARSs
20
R1: Use content-based CAMF-CC for a new item.
R2: Use demographics-based CAMF-CC for a new user.
R3: Average the predictions of content-based CAMF-CC and
demographics-based CAMF-CC for new contextual
situations or mixtures of cold-start cases.
54. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Adaptive Weighted adaptively sums the predictions of the basic algorithms
weighted by their estimated accuracies for the user, item and contextual
situation in question
• Extends the two-dimensional adaptive RS presented in (Bjørkøy, 2011)
• Conjecture: optimizes adaptation of differently performing CARS algorithms
Hybrid CARS Algorithms
Adaptive Weighted (1/2)
21
ˆr
…
∑
…
ˆr1
ˆr2
ˆrm
ˆa1
ˆa2
ˆam
55. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
56. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
57. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
58. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
59. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
60. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
61. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
ˆeuic1...ck
= (qi + xci
ci∈IC
∑ )T
(pu + ycu
cu ∈UC
∑ )+ ei + bu
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
average error for item i
bu baseline for user u
ei
62. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (1/2)
23
• Feature Weighted adaptively sums the
weighted predictions of the basic
algorithms with weights estimated using
meta-features, i.e., the number of user,
item and context ratings
• Is inspired by the Feature-Weighted
Linear Stacking (FWLS) algorithm (Sill et
al., 2009)
• Conjecture: exploits cold-start
conditions under which performance
differences between the CARS
algorithms can be observed
ˆv1
1
ˆa1
…
ˆr
∑
ˆr1
ˆrm
∑ ∑
…
…
…
…
f1 fn f1 fn
ˆv1
1
ˆvn
1
ˆv1
m
ˆvn
m
63. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard linear stacking, which is a method for linearly combining
the predictions of different models m ∈ M:
• Feature Weighted models the weight ŵm as a linear function of some meta-
features f ∈ F:
• The rating prediction function is rewritten as:
ˆruic1...ck
= ˆwm
m∈M
∑ ˆruic1...ck
m
ˆwm
= ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck )
ˆruic1...ck
= ( ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck ))
m∈M
∑ ˆruic1...ck
m
ˆruic1...ck
m
64. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard linear stacking, which is a method for linearly combining
the predictions of different models m ∈ M:
• Feature Weighted models the weight ŵm as a linear function of some meta-
features f ∈ F:
• The rating prediction function is rewritten as:
ˆruic1...ck
= ˆwm
m∈M
∑ ˆruic1...ck
m
ˆwm
= ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck )
ˆruic1...ck
= ( ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck ))
m∈M
∑ ˆruic1...ck
m
ˆruic1...ck
m
65. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard linear stacking, which is a method for linearly combining
the predictions of different models m ∈ M:
• Feature Weighted models the weight ŵm as a linear function of some meta-
features f ∈ F:
• The rating prediction function is rewritten as:
ˆruic1...ck
= ˆwm
m∈M
∑ ˆruic1...ck
m
ˆwm
= ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck )
ˆruic1...ck
= ( ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck ))
m∈M
∑ ˆruic1...ck
m
ˆruic1...ck
m
66. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard linear stacking, which is a method for linearly combining
the predictions of different models m ∈ M:
• Feature Weighted models the weight ŵm as a linear function of some meta-
features f ∈ F:
• The rating prediction function is rewritten as:
ˆruic1...ck
= ˆwm
m∈M
∑ ˆruic1...ck
m
ˆwm
= ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck )
ˆruic1...ck
= ( ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck ))
m∈M
∑ ˆruic1...ck
m
ˆruic1...ck
m
67. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard linear stacking, which is a method for linearly combining
the predictions of different models m ∈ M:
• Feature Weighted models the weight ŵm as a linear function of some meta-
features f ∈ F:
• The rating prediction function is rewritten as:
ˆruic1...ck
= ˆwm
m∈M
∑ ˆruic1...ck
m
ˆwm
= ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck )
ˆruic1...ck
= ( ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck ))
m∈M
∑ ˆruic1...ck
m
ˆruic1...ck
m
68. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard linear stacking, which is a method for linearly combining
the predictions of different models m ∈ M:
• Feature Weighted models the weight ŵm as a linear function of some meta-
features f ∈ F:
• The rating prediction function is rewritten as:
ˆruic1...ck
= ˆwm
m∈M
∑ ˆruic1...ck
m
ˆwm
= ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck )
ˆruic1...ck
= ( ˆvf
m
f ∈F
∑ f (u,i,c1,...,ck ))
m∈M
∑ ˆruic1...ck
m
ˆruic1...ck
m
69. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Evaluation
Used Datasets
25
• 4 contextually-tagged rating datasets
STS
(Braunhofer et al.,
2013)
CoMoDa
(Odić et al.,
2013)
Music
(Baltrunas et al.,
2011)
TripAdvisor
(www.tripadvisor.
com)
Domain POIs Movies Music POIs
Rating scale 1-5 1-5 1-5 1-5
Ratings 2,534 2,296 4,012 7,154
Users 325 121 43 5,487
Items 249 1,232 139 1,263
Contextual factors 14 12 8 3
Contextual conditions 57 49 26 31
Contextual situations 931 1,969 26 512
User attributes 7 4 10 2
Item features 1 7 2 2
70. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Evaluation
Evaluation Procedure
26
• Randomly divide the entities (i.e., users, items or contexts) into 10 cross-
validation folds
• For each fold k = 1, 2, …, 10
• Use all the ratings except those coming from entities in fold k as training
set to build the prediction models
• Calculate the Mean Absolute Error (MAE) and normalized Discounted
Cumulative Gain (nDCG) on the test ratings for the entities in fold k
• Advantage: allows to test the models on really cold entities
• Disadvantage: can’t test for different degrees of coldness
74. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train • Sensitive to poorly performing basic
algorithms
• Works only when all basic algorithms are
performing equally well
75. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train • Sensitive to poorly performing basic
algorithms
• Works only when all basic algorithms are
performing equally well
Heuristic
Switching
• Simple and fast to train
• Can avoid the impact of poorly performing
basic algorithms
• Depends on the manual choice of the
heuristic
76. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train • Sensitive to poorly performing basic
algorithms
• Works only when all basic algorithms are
performing equally well
Heuristic
Switching
• Simple and fast to train
• Can avoid the impact of poorly performing
basic algorithms
• Depends on the manual choice of the
heuristic
Adaptive
Weighted
• Adaptively combines the basic algorithms
based on their strengths and weaknesses
• Complex and slow to train
• Sensitive to the training set used
• Optimized for error minimization
• Sensitive to poorly performing basic
algorithms
77. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train • Sensitive to poorly performing basic
algorithms
• Works only when all basic algorithms are
performing equally well
Heuristic
Switching
• Simple and fast to train
• Can avoid the impact of poorly performing
basic algorithms
• Depends on the manual choice of the
heuristic
Adaptive
Weighted
• Adaptively combines the basic algorithms
based on their strengths and weaknesses
• Complex and slow to train
• Sensitive to the training set used
• Optimized for error minimization
• Sensitive to poorly performing basic
algorithms
Feature
Weighted
• Adaptively combines the basic algorithms
based on their strengths and weaknesses
• Robust in all cold-start cases
• Complex and slow to train
• Sensitive to the training set used
• Optimized for error minimization
78. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
31
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
79. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Conjecture: Active Learning (AL), which identifies the most useful items for
the target user to rate, can be improved for CARSs by leveraging the user’s
personality and by identifying the most useful contextual factors to be entered
upon rating these items
Active Learning for CARSs
32
item ratings
item ratings
request
approximated
function
supervised
learning
Active Learning
Passive Learning
user
80. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Conjecture: Active Learning (AL), which identifies the most useful items for
the target user to rate, can be improved for CARSs by leveraging the user’s
personality and by identifying the most useful contextual factors to be entered
upon rating these items
Active Learning for CARSs
32
item ratings
item ratings
request
approximated
function
supervised
learning
Active Learning
Passive Learning
personality
(Big-5)
user
81. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Conjecture: Active Learning (AL), which identifies the most useful items for
the target user to rate, can be improved for CARSs by leveraging the user’s
personality and by identifying the most useful contextual factors to be entered
upon rating these items
Active Learning for CARSs
32
item ratings
item ratings
request
approximated
function
supervised
learning
Active Learning
Passive Learning
personality
(Big-5)
user
+ context data
+ context data request
82. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Using Personality in Active Learning
• Main idea: people with similar personality are likely to have similar interests
(Rentfrow & Gosling, 2003), and thus the incorporation of human personality can
help in predicting the items that can be rated by a user
33
Neuroticism
Conscientious-
ness
Openness
ExtraversionAgreeableness
Big Five
Personality
Traits
83. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Personality-Based Binary Prediction
• Input: Target user u. Maximum number of items to be returned N. Binary user-
item rating matrix B. Candidate set of items to be rated Cu
• Output: List of M <= N top-scoring items for which user u is requested to
provide ratings
34
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user u’s attributes (i.e., Big-5 scores)
ya latent factor vector of user attribute a
average binary rating for item i
bu baseline for user u
xi
ˆxui = qi
T
(pu + ya )
a∈A(u)
∑ + xi + bu
84. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Personality-Based Binary Prediction
• Input: Target user u. Maximum number of items to be returned N. Binary user-
item rating matrix B. Candidate set of items to be rated Cu
• Output: List of M <= N top-scoring items for which user u is requested to
provide ratings
34
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user u’s attributes (i.e., Big-5 scores)
ya latent factor vector of user attribute a
average binary rating for item i
bu baseline for user u
xi
ˆxui = qi
T
(pu + ya )
a∈A(u)
∑ + xi + bu
85. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Personality-Based Binary Prediction
• Input: Target user u. Maximum number of items to be returned N. Binary user-
item rating matrix B. Candidate set of items to be rated Cu
• Output: List of M <= N top-scoring items for which user u is requested to
provide ratings
34
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user u’s attributes (i.e., Big-5 scores)
ya latent factor vector of user attribute a
average binary rating for item i
bu baseline for user u
xi
ˆxui = qi
T
(pu + ya )
a∈A(u)
∑ + xi + bu
86. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Parsimonious & Adaptive Context Acquisition
• Main idea: for each user-item pair (u, i),
identify the contextual factors that
when acquired with u’s rating for i
improve most the long term
performance of the recommender
• Heuristic: acquire the contextual
factors that have the largest impact
on rating prediction
• Challenge: how to quantify these
impacts?
35
87. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
CARS Prediction Model
• We use the new variant of CAMF that we already successfully employed to
estimate the rating prediction accuracy of a CARS algorithm
• Advantage: allows to capture latent correlations and patterns between a
potentially wide range of knowledge sources ⟹ ideal to derive the usefulness
of contextual factors
36
ˆruic1...ck
= (qi + xa
a∈A(i)∪C(i)
∑ )T
⋅(pu + yb
b∈A(u)∪C(u)
∑ )+ ri + bu
qi latent factor vector of item i
A(i) set of conventional item attributes (e.g., genre)
C(i) set of contextual item attributes (e.g., weather)
xa latent factor vector of item attribute a
pu latent factor vector of user u
A(u) set of conventional user attributes (e.g., age)
C(u) set of contextual user attributes (e.g., mood)
yb latent factor vector of user attribute b
ṝi average rating for item i
bu baseline for user u
88. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each contextual condition cj ∈ Cj
by calculating the absolute deviation between the rating prediction when the
condition holds (i.e., ȓuicj) and the predicted context-free rating (i.e., ȓui):
where fcj is the normalized frequency of cj
• Finally, it computes for each factor the average of these deviation scores, and
selects the contextual factors with the largest average scores
37
ˆwuicj
= fcj
ˆruicj
− ˆrui ,
89. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each contextual condition cj ∈ Cj
by calculating the absolute deviation between the rating prediction when the
condition holds (i.e., ȓuicj) and the predicted context-free rating (i.e., ȓui):
where fcj is the normalized frequency of cj
• Finally, it computes for each factor the average of these deviation scores, and
selects the contextual factors with the largest average scores
37
ˆwuicj
= fcj
ˆruicj
− ˆrui ,
90. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each contextual condition cj ∈ Cj
by calculating the absolute deviation between the rating prediction when the
condition holds (i.e., ȓuicj) and the predicted context-free rating (i.e., ȓui):
where fcj is the normalized frequency of cj
• Finally, it computes for each factor the average of these deviation scores, and
selects the contextual factors with the largest average scores
37
ˆwuicj
= fcj
ˆruicj
− ˆrui ,
91. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each contextual condition cj ∈ Cj
by calculating the absolute deviation between the rating prediction when the
condition holds (i.e., ȓuicj) and the predicted context-free rating (i.e., ȓui):
where fcj is the normalized frequency of cj
• Finally, it computes for each factor the average of these deviation scores, and
selects the contextual factors with the largest average scores
37
ˆwuicj
= fcj
ˆruicj
− ˆrui ,
92. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiments 1 and 2
• 2 user studies involving 108 subjects in the 1st and 51 subjects in the 2nd
• Compared personality-based binary prediction with log(popularity) *
entropy and random
• Personality-based binary prediction performed best in terms of:
• Number of acquired ratings
• Rating prediction accuracy
• Quality of context-aware recommendations
38
93. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Datasets
39
• 3 contextually-tagged rating datasets
CoMoDa
(Odić et al.,
2013)
TripAdvisor
(www.tripadvisor.
com)
STS
(Braunhofer et al.,
2013)
Domain Movies POIs POIs
Rating scale 1-5 1-5 1-5
Ratings 2,098 4,147 2,534
Users 112 3,916 325
Items 1,189 569 249
Contextual factors 12 3 14
Contextual conditions 49 31 57
Avg. # of conditions / rating 12 3 1.49
User attributes 4 2 7
Item features 7 2 1
95. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
96. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
97. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
98. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
99. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
100. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
101. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
102. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the testing
set, after training the prediction model on the new extended training set
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
103. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the testing
set, after training the prediction model on the new extended training set
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors, if any
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set Testing set
• Repeated random sub-sampling validation (20 times):
• Randomly partition the ratings into three subsets
• Repeat
104. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
user-item pair
top two contextual factors
rating transferred to training set
+
+
=
rating in candidate set
105. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
top two contextual factors
rating transferred to training set
+
+
=
rating in candidate set
106. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
Season and Weather
rating transferred to training set
+
+
=
rating in candidate set
107. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
Season and Weather
rating transferred to training set
rAlice Skiing Winter Sunny Warm Morning = 5+
+
=
108. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
Season and Weather
rAlice Skiing Winter Sunny Warm Morning = 5
rAlice Skiing Winter Sunny = 5
+
+
=
109. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Baseline Methods for Evaluation
42
• Mutual Information: given a user-item pair (u,i), computes the relevance for a
contextual factor Cj as the mutual information between ratings for items
belonging to i’s category (Baltrunas et al., 2012)
• Freeman-Halton Test: calculates the relevance of Cj using the Freeman-
Halton test (Odić et al., 2013)
• Minimum Redundancy Maximum Relevance (mRMR): ranks each Cj
according to its relevance to the rating variable and redundancy to other
contextual factors (Peng et al., 2005)
110. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Results: Prediction Accuracy
43
CoMoDa
U-MAE
0.71
0.72
0.73
0.74
0.75
0.76
0.77
0.78
0.79
0.80
0.81
0.82
1 2 3 4
Mutual Information Freeman-Halton mRMR Largest Deviation All factors
STS
0.90
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1.00
1 2 3 4
Stars denote significant improvements of Largest Deviation over the other considered algorithms
(p < 0.05)
*
*
* * *
* * *
111. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Results: Ranking Quality
44
CoMoDa
Precision@10
0.0000
0.0002
0.0004
0.0006
0.0008
0.0010
0.0012
0.0014
0.0016
1 2 3 4
Mutual Information Freeman-Halton mRMR Largest Deviation All factors
STS
0.005
0.006
0.007
0.008
0.009
0.010
0.011
0.012
0.013
0.014
0.015
0.016
1 2 3 4
*
*
*
*
*
*
*
*
Stars denote significant improvements of Largest Deviation over the other considered algorithms
(p < 0.05)
112. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Results: # of Acquired Conditions
45
STS
Avg#ofacquiredconditions
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1 2 3 4
Mutual Information Freeman-Halton mRMR Largest Deviation All factors
* * * * *
* * *
* *
*
*
Stars denote significant improvements of Largest Deviation over the other considered algorithms
(p < 0.05)
113. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
46
• Context-Aware Recommenders and the Cold-Start Problem
• State of the Art
• South Tyrol Suggests Application Scenario
• Hybrid Context-Aware Recommendation Algorithms
• Active Learning for Context-Aware Recommenders
• Conclusions and Future Work
114. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Conclusions
• Novel hybrid recommendation algorithms that, in many cases, effectively
alleviate the cold-start problem of CARS
• New personality-based Active Learning rating acquisition algorithm that can
better estimate what items a (new) user is able to rate
• Novel parsimonious and adaptive context acquisition algorithm that can
identify what contextual factors to acquire from the user upon rating an item,
thus minimizing the user’s rating effort
• Comprehensive evaluation of the proposed solutions in cold-start scenarios
47
115. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Future Work
• Additional experiments and datasets
• Improvement of proposed algorithms
• Proactive Active Learning
• Sequential Active Learning
• Gamification approaches
48
116. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Journal Papers
Fernández-Tobías, I., Braunhofer, M., Elahi, M., Cantador, I., & Ricci, F. (2016). Alleviating the New
User Problem in Collaborative Filtering by Exploiting Personality Information. User Modeling and
User-Adapted Interaction, 1-35. http://dx.doi.org/10.1007/s11257-016-9172-z
Braunhofer, M., Elahi, M., & Ricci, F. (2014). Techniques for cold-starting context-aware mobile
recommender systems for tourism. Intelligenza Artificiale, 8(2), 129-143. http://dx.doi.org/10.3233/
IA-140069
Braunhofer, M., Kaminskas, M., & Ricci, F. (2013). Location-aware music recommendation.
International Journal of Multimedia Information Retrieval, 2(1), 31-44. http://dx.doi.org/10.1007/
s13735-012-0032-2
49
117. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Conference Papers
Nasery, M., Braunhofer, M., & Ricci, F. (2016). Recommendations with Optimal Combination of
Feature-Based and Item-Based Preferences. To appear in User Modeling, Adaptation, and
Personalization. Halifax, Canada: Springer International Publishing
Braunhofer, M., & Ricci, F. (2016). Contextual Information Elicitation in Travel Recommender
Systems. In Information and Communication Technologies in Tourism 2016 (pp. 579-592). Bilbao,
Spain: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-28231-2_42 (Second
Best Research Paper Award)
Braunhofer, M., Elahi, M., & Ricci, F. (2015). User Personality and the New User Problem in a
Context-Aware Points of Interest Recommender System. In Information and Communication
Technologies in Tourism 2015 (pp. 537-549). Lugano, Switzerland: Springer International Publishing.
http://dx.doi.org/10.1007/978-3-319-14343-9_39
Braunhofer, M., Elahi, M., & Ricci, F. (2014). Usability Assessment of a Context-Aware and
Personality-Based Mobile Recommender System. In E-Commerce and Web Technologies (pp. 77-88).
Munich, Germany: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-10491-1_9
Braunhofer, M., Elahi, M., Ge, M., & Ricci, F. (2014). Context Dependent Preference Acquisition with
Personality-Based Active Learning in Mobile Recommender Systems. In Learning and Collaboration
Technologies. Technology-Rich Environments for Learning and Collaboration, Held as Part of HCI
International 2014 (pp. 105-116). Heraklion, Crete, Greece: Springer International Publishing. http://
dx.doi.org/10.1007/978-3-319-07485-6_11
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118. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Conference Papers (contd.)
Braunhofer, M., Codina, V., & Ricci, F. (2014). Switching hybrid for cold-starting context-aware
recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems
(pp. 349-352). Foster City, Silicon Valley, California, USA: ACM. http://dx.doi.org/
10.1145/2645710.2645757
Braunhofer, M., Elahi, M., Ricci, F., & Schievenin, T. (2013). Context-aware points of interest
suggestion with dynamic weather data management. In Information and Communication
Technologies in Tourism 2014 (pp. 87-100). Dublin, Ireland: Springer International Publishing.
http://dx.doi.org/10.1007/978-3-319-03973-2_7
Elahi, M., Braunhofer, M., Ricci, F., & Tkalcic, M. (2013). Personality-based active learning for
collaborative filtering recommender systems. In AI*IA 2013: Advances in Artificial Intelligence (pp.
360-371). Turin, Italy: Springer International Publishing. http://dx.doi.org/
10.1007/978-3-319-03524-6_31
Enrich, M., Braunhofer, M., & Ricci, F. (2013). Cold-Start Management with Cross-Domain
Collaborative Filtering and Tags. In E-Commerce and Web Technologies (pp. 101-112). Prague,
Czech Republic: Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-39878-0_10
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119. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Workshop, Demo & Doctoral Consortium Papers
Braunhofer, M., Fernández-Tobías, I., & Ricci, F. (2015). Parsimonious and Adaptive Contextual
Information Acquisition in Recommender Systems. In Proceedings of the Joint Workshop on
Interfaces and Human Decision Making for Recommender Systems, IntRS 2015, co-located with
ACM Conference on Recommender Systems (RecSys 2015). Vienna, Austria: ACM.
Braunhofer, M., Ricci, F., Lamche, B., & Wörndl, W. (2015). A Context-Aware Model for Proactive
Recommender Systems in the Tourism Domain. In Proceedings of the 17th International
Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp.
1070-1075). Copenhagen, Denmark: ACM. http://dx.doi.org/10.1145/2786567.2794332
Braunhofer, M. (2014). Hybridisation techniques for cold-starting context-aware recommender
systems. In Proceedings of the 8th ACM Conference on Recommender systems, Doctoral
Symposium (pp. 405-408). Foster City, Silicon Valley, California, USA: ACM. http://dx.doi.org/
10.1145/2645710.2653360
Braunhofer, M. (2014). Hybrid solution of the cold-start problem in context-aware recommender
systems. In User Modeling, Adaptation, and Personalization, Doctoral Consortium (pp. 484-489).
Aalborg, Denmark: Springer International Publishing. http://dx.doi.org/
10.1007/978-3-319-08786-3_44
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120. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Workshop, Demo & Doctoral Consortium Papers (contd.)
Braunhofer, M., Elahi, M., & Ricci, F. (2014). STS: A Context-Aware Mobile Recommender System
for Places of Interest. In Extended Proceedings of User Modeling, Adaptation, and Personalization
(pp. 75-80). Aalborg, Denmark.
Braunhofer, M., Elahi, M., Ge, M., Ricci, F., & Schievenin, T. (2013). STS: Design of Weather-Aware
Mobile Recommender Systems in Tourism. In Proceedings of the First International Workshop on
Intelligent User Interfaces: Artificial Intelligence meets Human Computer Interaction (AI*HCI 2013).
A workshop of the XIII International Conference of the Italian Association for Artificial Intelligence
(AI*IA 2013). Turin, Italy.
53