The document describes research on enhancing recommender systems through the use of user profiles and tagging systems. It discusses how user profiles can be used to provide personalized recommendations by describing a user's interests. It presents two research papers that studied how profile similarity and rating overlap between users can improve recommendation accuracy and user confidence. It also discusses how tagging systems can be leveraged by integrating user, tag, and resource dimensions. One paper proposes a personalized recommender model for folksonomies that extends the folksonomy by combining shared tags/resources and recommends tags and resources based on a user's profile and tagging history.
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
Types of recommender systems in information retrieval. Collaborative filtering is a very widely used method in recommendation systems. Content based filtering and collaborative filtering are two major approaches. Hybrid systems are now being employed to get better recommendations. One such method is content-boosted collaborative filtering.
Discovering User's Topics of Interest in Recommender SystemsGabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling.
Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We present some of Smart Canvas features powered by its recommender system, such as:
- Highlight relevant content, explaining to the users which of his topics of interest have generated each recommendation.
- Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests.
- Recommends people with similar interests, explaining which topics brings them together.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Massive Data Analysis- Challenges and ApplicationsVijay Raghavan
We highlight a few trends of massive data that are available for corporations, government agencies and researchers and some examples of opportunities that exist for turning this data into knowledge. We provide a brief overview of some of the state-of-the-art technologies in the massive data analysis landscape. Then, we describe two applications from two diverse areas in detail: recommendations in e-commerce, link discovery from biomedical literature. Finally, we present some challenges and open problems in the field of massive data analysis.
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
Types of recommender systems in information retrieval. Collaborative filtering is a very widely used method in recommendation systems. Content based filtering and collaborative filtering are two major approaches. Hybrid systems are now being employed to get better recommendations. One such method is content-boosted collaborative filtering.
Discovering User's Topics of Interest in Recommender SystemsGabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling.
Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We present some of Smart Canvas features powered by its recommender system, such as:
- Highlight relevant content, explaining to the users which of his topics of interest have generated each recommendation.
- Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests.
- Recommends people with similar interests, explaining which topics brings them together.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Massive Data Analysis- Challenges and ApplicationsVijay Raghavan
We highlight a few trends of massive data that are available for corporations, government agencies and researchers and some examples of opportunities that exist for turning this data into knowledge. We provide a brief overview of some of the state-of-the-art technologies in the massive data analysis landscape. Then, we describe two applications from two diverse areas in detail: recommendations in e-commerce, link discovery from biomedical literature. Finally, we present some challenges and open problems in the field of massive data analysis.
The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences.
The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited.
Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed.
For full details about the presented approaches please refer to the full papers mentioned in this presentation.
Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR).
Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity.
The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas.
genetic algorithm based music recommender systemneha pevekar
The goal of a recommender
system is to generate meaningful recommendations to
a collection of users for items or products that might
interest them.
Many of the largest e-commerce websites are already
using recommender systems to help their customers
find products to purchase or download.
Spotify uses a range of Machine Learning models to power its music recommendation features including the Discover page and Radio. Due to the iterative nature of training these models they suffer from IO overhead of Hadoop and are a natural fit to the Spark programming paradigm. In this talk I will present both the right way as well as the wrong way to implement collaborative filtering models with Spark. Additionally, I will deep dive into how Matrix Factorization is implemented in the MLlib library.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
Presentation given for one of Pearson's Data Research teams. It motivates the use of recommender systems, describes common approaches to building and evaluating them and gives examples of how they are used in Mendeley. Thanks to Maya Hristakeva for creating some of the slides.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
Taking the Pain out of Data Science - RecSys Machine Learning Framework Over ...Sonya Liberman
Outbrain is the world’s largest discovery platform, bringing personalized and relevant content to audiences while helping publishers understand their audiences through data.
Its recommender system is serving billions of content recommendations daily, based on millions of hourly user interactions.
Our predictive models span over a variety of supervised learning techniques, ranging from content-based recommenders, through behavioral models and all the way to collaborative techniques such as factorization machines. Agility and stability are crucial aspects of the system.
This talk will cover our journey towards solutions that would not compromise neither on scale nor on model complexity, and design a dynamic framework that shortens the cycle between research and production.
We will cover the different stages of the framework, including important take away lessons for data scientists as well as software engineers.
Sonya Liberman is leading a team of Machine Learning Engineers and Data Scientists building large-scale recommender systems for personalized content discovery @ Outbrain, serving tens of billions real-time recommendations a day.
Especially enjoys bringing theory to production and seeing how it affects the engagement of (many) users.
This invited talk was given at ILTechTalk Week, 2018 by Shaked Bar, a Teach Lead and Algorithms Engineer in the team.
Lucene/Solr Revolution 2015: Where Search Meets Machine LearningJoaquin Delgado PhD.
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Lucene/Solr Revolution 2015: Where Search Meets Machine LearningS. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
2. Образец заголовкаOverview
1. The Recommender System
2. Traditional Recommendation Methods: definition, pros, and
cons
1) Collaborative Filtering
2) Content-based Recommendations
3) Knowledge-based systems
4) Hybrid Approaches
3. Enhance Recommender Systems with User Profiles
– Research papers
4. Leveraging Tagging Systems with User Information
– Research papers
5. Tutorial Conclusions
6. Acknowledgements
6. Образец заголовкаThe Recommender System
• Traditional definition: Estimate a utility
function that automatically predicts how a
user will like an item.
• Based on:
– Past behavior
– Relations to other users
– Item similarity
– Context
– …
9. Образец заголовкаCollaborative Filtering
• Widely used in e-commerce
• Find users in a community that share the
same interests in the past to predict what
the current user will be interested in.
12. Образец заголовкаUser-Based CF
• A collection of user ui , i=1, …, n and a collection of
products pj , j=1, …, m
• An n × m matrix of ratings vij , with vij = ? if user i did not
rate product j
• Prediction for user i and product j is computed
• Similarity can be computed by Pearson correlation
15. Образец заголовкаItem-Based CF
1. Look into the items the target user has rated
2. Compute how similar they are to the target
item
– Similarity only using past ratings from other users
3. Select k most similar items
4. Compute Prediction by taking weighted
average on the target user’s ratings on the
most similar items
16. Образец заголовкаItem Similarity Computation
• Cosine-based Similarity (difference in
rating scale between users is not taken
into account)
• Adjusted Cosine Similarity (takes care of
difference in rating scale)
U = set of users that rated both items a and b
19. Образец заголовкаMemory-Based CF
• Use the entire user-item database to
generate a prediction
• Usage of statistical techniques to find the
neighbors – e.g. nearest-neighbor.
20. Образец заголовкаModel-Based CF
• First develop a model of user
• Type of model:
– Probabilistic (e.g. Bayesian Network)
– Clustering
– Rule-based approaches (e.g. Association Rules)
– Classification
– Regression
– LDA
– …
21. Образец заголовкаPros & Cons
Pros:
• Requires minimal knowledge engineering efforts
• Users and products are symbols without any internal structure or
characteristics
• Produces good-enough results in most cases
Cons:
• Sparsity – evaluation of large itemsets
where user/item interactions are under
1%
• Scalability - Nearest neighbor require
computation that grows with both the
number of users and the number of
items
24. Образец заголовкаContent-Based Recommenders
• Recommendations based on content of
items rather than on other users’
opinions/interactions
• Common for recommending text-based
products
25. Образец заголовкаSimilarity-Based Retrieval
• Nearest Neighbors
• Relevance Feedback and Rocchio’s
Algorithm
• Probabilistic approaches based on Naïve
Bayes
• Linear classifiers and machine learning
• Decision Tree
26. Образец заголовкаHow they work?
• Items to recommend are “described” by
their associated features (e.g. keywords)
• User Model structured in a “similar” way as
the content: features/keywords more likely
to occur in the preferred documents (lazy
approach)
• The user model can be a classifier based
on whatever technique (Neural Networks,
Naïve Bayes...)
27. Образец заголовкаPros & Cons
• Pros
– User independence
• No cold-start or sparsity
– Able to recommend to users with unique tastes
– Able to recommend new and unpopular items
– Can provide explanations by listing content-features
• Cons
– Requires content that can be encoded as meaningful
features (difficult in some domains/catalogs)
– Users represented as learnable function of content features
– Difficult to implement serendipity
– Easy to overfit (e.g. for a user with few data points)
28. Образец заголовкаCF vs. CB
CF CB
Compare Users interest Item info
Similarity Set of users
User profile
Item info
Text document
Shortcoming Other users’ feedback matters
Coverage
Unusual interest
Feature matters
Over-specialize
Eliciting user feedback
31. Образец заголовкаKnowledge-Based Systems
• Select items from the catalog that fulfill a
set of applicable constraints specified by
the user
• Two basic types:
– Constraint-based
– Case-based
32. Образец заголовкаPseudocode
1. Users specify the requirements
2. Systems try to identify solutions
3. If no solution can be found, users change
requirements
33. Образец заголовкаConstraint-Based vs. Case-Based
• Case-based:
– Based on different types of similarity measures
– Retrieve items that are similar to specified
requirements
• Constraint-based:
– Rely on explicitly defined set of rules
– Retrieve items that fulfill the rules
– Critiquing is an effective way to support
navigation in item space to find useful alternatives
34. Образец заголовкаPros & Cons
• Pros
– Cold-start problem doesn’t exist
• recommendations are calculated independently of user ratings
– Does not have to gather information about a particular
user
• Judgments are independent of individual tastes
• Cons
– High cost and effort
– The nature of knowledge
• Knowledge is specific to the domain
• Can not be shared without the presence of expert even the
knowledge is available
– The level of risk
• Development cost is very high
• Cost goes higher and higher in maintaining these systems
37. Образец заголовка
Hybrid Recommender Systems:
Survey and Experiments
• Well-known survey of the design space of
different hybrid recommendation algorithms
by Robin Burke
• Proposes a taxonomy of different classes of
recommendation algorithms
• Seven different hybridization strategies can
be abstracted into three base designs:
– Monolithic hybrids
– Parallelized hybrids
– Pipelined hybrids
38. Образец заголовкаMonolithic
• Incorporates aspects of several
recommendation strategies in one algorithm
implementation
• Data-specific preprocessing steps are used to
transform the input data into a
representation that can be exploited by a
specific algorithm paradigm
• Advantageous if little additional knowledge is
available for inclusion on the feature level
39. Образец заголовкаMonolithic
• Feature combination hybrid
– uses a diverse range of input data
• Feature augmentation hybrid
– integrate several recommendation algorithms
40. Образец заголовкаParallelized
• Employ several recommenders side by side
and employ a specific hybridization
mechanism to aggregate their outputs
• Least invasive to existing implementations
• Act as an additional post-processing step
41. Образец заголовкаParallelized
• Mixed
– combines the results of different recommender systems at
the level of the user interface
– results from different techniques are presented together.
• Weighted
– combines the recommendations of two or more
recommendation systems by computing weighted sums of
their scores.
• Switching
– require an oracle that decides which recommender should
be used in a specific situation, depending on the user
profile and/or the quality of recommendation results.
42. Образец заголовкаPipelined
• Implement a staged process in which
several techniques sequentially build one
another before the final one produces
recommendations for the user
• Most ambitious hybridization designs
• Require deeper insight into algorithm’s
functioning to ensure efficient runtime
computations
43. Образец заголовкаPipelined
• Cascade hybrids
– based on a sequenced order of techniques
– each succeeding recommender only refines
the recommendations of its predecessor
• Meta-level hybridization design
– one recommender builds a model that is
exploited by the principal recommender to
make recommendations
48. Образец заголовкаWhy Using User Profile?
• A profile of the user's interests is used by
most recommendation systems
• Used to provide personalized
recommendations
• Describes the types of items the user likes
• Compares items to the user profile to
determine what to recommend
• Created and updated automatically in
response to feedback on the desirability of
items that have been presented to the user
49. Образец заголовка
Accounting for Taste: Using Profile
Similarity to Improve
Recommender Systems
Philip Bonhard , Clare Harries , John McCarthy ,
M. Angela S
50. Образец заголовкаBackground
• User-user collaborative filtering comes closest to
emulating real world recommendations
– based on user rather than item matching
• Recommender system research focus:
– Precision effectiveness: tested against the real ratings
– Prediction efficiency: computational cost in terms of
time and resources for calculating predictions
• Recommender systems can be made more
effective and usable by appropriating some
functionality from social systems
Philip Bonhard , Clare Harries , John McCarthy , M. Angela Sasse, Accounting for taste: using profile similarity to improve recommender systems
51. Образец заголовкаExperiment
• Independent variables: recommender profile
characteristics
– familiarity, profile similarity, and rating overlap
• Dependent variable: choices people make in a
recommender system context
• Hypotheses and results:
1. Familiar recommenders will be preferred
– not supported
2. Similar recommenders will be preferred
– overwhelmingly supported
3. Recommenders with high rating overlap will be
preferred
– supported
Philip Bonhard , Clare Harries , John McCarthy , M. Angela Sasse, Accounting for taste: using profile similarity to improve recommender systems
52. Образец заголовкаResults
Philip Bonhard , Clare Harries , John McCarthy , M. Angela Sasse, Accounting for taste: using profile similarity to improve recommender systems
53. Образец заголовкаConclusions
• Rating overlap in combination with profile
similarity can be a powerful source of
information for a decision-maker when
judging the validity of a recommendation
• Participants were more confident in their
choices when the recommender had a high
rating overlap with them in combination with
a similar profile
• Decision-makers trust recommenders more
when they have high rating overlap and a
similar profile
Philip Bonhard , Clare Harries , John McCarthy , M. Angela Sasse, Accounting for taste: using profile similarity to improve recommender systems
56. Образец заголовкаTagging
• The process of assigning metadata in the
form of keywords to shared content by
many users
• An important way to provide information
about resources on the Web
• Enable the organization of information
within personal information spaces that
can be shared
57. Образец заголовкаCollaborative Tagging Systems
• Folksonomies
• Allow users to tag documents, share their
tags, and search for documents based on
these tags
• Collaborative tagging
– tagging of a collection of documents
commonly accessible to a large group
• Social bookmarking
– tagging contents located all over the Web
58. Образец заголовкаTag Recommendation
• Recommend relevant tags for an untagged user
resource
• Integrative models that leverage all three
dimensions of a social annotation system (users,
resources, tags) produce superior results
• Various purposes:
– Increase the chances of getting a resource annotated
– Remind users what a resource is about
– Lazy annotation
– …
59. Образец заголовка
Benefits of Collaborative Tagging
Systems
• Lowers costs
– no complicated, hierarchically organized
nomenclature to learn
• Respond quickly to changes and innovations
in the way users categorize content
– inherently open-ended
• Allow a user to search for the content that
the user has tagged using a personal
vocabulary
• Assist navigation by providing dynamic
hyperlinks among tags, documents and users
60. Образец заголовка
Challenges of Collaborative Tagging
Systems
• Too much freedom of choice of tags
– Polysemy: words having multiple related meanings
– Synonymy: multiple words having the same or similar meanings
• Challenges in support knowledge management activities in an
organization
• Challenges in identifying communities of common interest
• Challenges in identifying information leaders or domain
experts
• Lack of a document hierarchy prevents it from being widely
adopted by enterprises
– Organizations need systematic mechanisms of storing and
retrieving documents
61. Образец заголовка
A Personalized Recommender
System Based on Users’
Information In Folksonomies
Mohamed Nader Jelassi, Sadok Ben Yahia,
Engelbert Mephu Nguifo
62. Образец заголовкаMotivation
• Success of social bookmarking sharing
systems
– Flickr, Bibsonomy, Youtube, etc.
• The users of a folksonomy have different
profiles and expectations depending on
their motivations
• Personalization provides solutions to help
users solve the information overload issue
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
63. Образец заголовка
Personalized Recommendation in
Folksonomies
• Extend the folksonomy
• Combine both shared tags/resources
– quadratic concepts
– bring maximal shared sets of users, tags and
resources
• Personalize tags/resources recommendations
– Users’ profile as a new dimension
– look for both users’ profile and tagging history
before making recommendation
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
64. Образец заголовкаQuadratic Concepts
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
65. Образец заголовкаSteps
• Inputs: a set of frequent quadri-concepts, a user u with
its profile p and optionally a resource r to annotate
• Outputs: a set of proposed users, suggested tags and
recommended resources
• User Proposition Step
– seeks for quadri- concepts whose users have the same
profile
• Tag Suggestion Step
– suggest personalized tags to a target user that share a
resource in the p-folksonomy
• Resource Recommendation Step
– propose a personalized list of resources to a targeted user
that is susceptible to be in accordance with its interests
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
66. Образец заголовкаAlgorithm
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
67. Образец заголовкаEvaluation
• MovieLens dataset
– with examples of extracted quadri-concepts
following different profiles of folksonomy’ users
• 50,000 users
• 95,580 tags applied to 10,681 movies by
71,567 users
• Additional user information available:
– Gender, profession, age
• Training set/Test set
– 80% as training set
– 20% as validation data
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
68. Образец заголовкаResults
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
69. Образец заголовкаResults and Conclusions
• In an average of 38% outperforms the
precision of the approach of Liang et al.,
which is between 24% and 30%
• Best performances obtained with k=5
• Quadratic concepts improves the
recommendations by suggesting tags and
resources the more specific to users’ needs
Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Nguifo, A Personalized Recommender System Based on Users’ Information In Folksonomies
70. Образец заголовка
Hybrid tag recommendation for
social annotation system
Jonathan Gemmell, Thomas Schimoler,
Bamshad Mobasher, Robin Burke
71. Образец заголовкаData Model
• Record of a user labeling a resource with one or
more tags
• Collection of annotations results in a complex
network of interrelated users, resources and tags
• Social annotation system
– Can be described as a four-tuple: U, R, T, A
– Can be viewed as a three dimensional matrix: U, R, T
• U: a set of users
• R: a set of resources
• T: a set of tags
• A: a set of annotations
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
72. Образец заголовка
Linear Weighted Hybrid Tag
Recommender
• Aggregates the results of several component
recommenders in linear combination
• View each component of a tag
recommendation system as a function
• To produce a ranked list of suggested tags for
a particular user given a specific resource:
• Relevance score for a tag is calculated using
several component tag recommenders
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
73. Образец заголовка
Linear Weighted Hybrid Tag
Recommender
• Specializes in only a few available
dimensions of the data
• Focus on relatively simple component
recommenders due to their speed and
scrutability
• Discussed components:
– Popularity Models
– User-Based Collaborative Filtering
– Item-Based Collaborative Filtering
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
74. Образец заголовкаComponent 1: Popularity Models
• Recommend the most popular tags
• Strictly resource dependent
• Does not take into account the tagging habits of
the user
• Serve as a baseline and may benefit the hybrid
• Require little online computation
• Easily built offline and can be incrementally
updated
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
75. Образец заголовкаComponent 1: Popularity Models
• Resource based popularity recommender
• User based popularity recommender
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
76. Образец заголовкаComponent 2: User-based CF
• Works under the assumption that users who have agreed in
the past are likely to agree in the future
• Relies on the collaboration of other users
• Only recommends tags applied to the query resource
• Narrows the focus of the recommendation regardless of the
diversity in the user profile
• Advantages:
– Personalization
• Disadvantages:
– Cannot recommend tags that do not appear in a neighbor’s
profile
– Lacks the ability to reflect the habits and patterns of the larger
crowd
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
77. Образец заголовкаComponent 3: Item-Based CF
• Relies on discovering similarities among resources
rather than among users
• Similarity metrics only calculated with resources in
the user profile
• Constructs a neighborhood of resources from the
user profile most similar to the query resource
• Effectively ignores parts of the user profile not
relevant to the recommendation task
• Advantages:
– Computation can be quickly done in real time
– Similarities can be calculated offline for large user
profile
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
78. Образец заголовкаEvaluation
• Datasets
– Bibsonomy, Citeulike, MovieLens, Delicious,
Amazon, LastFM
• Methodology
1. Each user’s annotations were divided equally
among five folds
2. The recommenders are evaluated on their ability
to recommend tags given a user-resource pair
3. Evaluate returned tags against the tags in the
holdout annotation
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
79. Образец заголовкаResults
• Integrative approach can exploit multiple
dimensions of the data
• Hybrid outperforms a state-of-the-art model-
based algorithm based on tensor
factorization (PITF)
– particularly when the user profiles are diverse
• Social annotation systems vary in how users
interact with the system
• The differences between datasets make the
performance of individual recommenders
unpredictable
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
80. Образец заголовкаAdvantages of the Proposed Hybrid System
• More efficient, scalable, extensible and
explainable than PITF
• The proposed linear weighted hybrid
inherits the capacity to focus on specific
aspects of the user profile
• Constructed from simple yet fast
components
• Offers a highly scalable and easily
updatable solution for tag
recommendation
Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke, Hybrid tag recommendation for social annotation system
82. Образец заголовкаMotivation
• Tags are used to enable the organization
of information within personal information
spaces that can also be shared
• Tag distributions stabilize over time and
can be used to improve search on the Web
• Question: How tags can characterize the
user and enable personalized
recommendations?
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
83. Образец заголовкаExperiment
• Dataset: Last.fm
• Crawled subset of the Last.fm website, including
pages corresponding to tags, music tracks and
user profiles
• Used track-based and tag-based profiles to
evaluate different algorithms for producing music
recommendations
– Track-based user profiles: collections of music tracks
with associated preference scores, describing users’
musical tastes
– Tag-based user profiles: collections of tags together
with corresponding scores representing the user’s
interest in each of these tags
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
85. Образец заголовкаAlgorithms
• 7 algorithms based on the type of profile and the technique
used for getting the recommendations
• three categories:
– Collaborative Filtering based on Tracks
– Collaborative Filtering based on Tags
– Search based on Tags
• Tag-based recommendation algorithms:
– CF based on Track-Tags with ITF (CFTTI)
– CF based on Track-Tags No-ITF (CFTTN)
– CF based on Tags (CFTG)
• Tag-Based Search algorithms
– Search based on Track-Tags with ITF (STTI)
– Search based on Track-Tags No-ITF (STTN)
– Search based on Tags (STG)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
86. Образец заголовка
CF based on Track-Tags with ITF
(CFTTI)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
87. Образец заголовка
CF based on Track-Tags No-ITF
(CFTTN)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
• Differs from CFTTI by computing the tag
based profiles without the IT F parameter
in the formula corresponding to tags’
preference
88. Образец заголовкаCF based on Tags (CFTG)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
89. Образец заголовка
Search based on Track-Tags with ITF
(STTI)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
90. Образец заголовка
Search based on Track-Tags No-ITF
(STTN)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
• Remove the ITF parameter in the
preference formula
91. Образец заголовкаSearch based on Tags (STG)
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
92. Образец заголовкаEvaluation
• 18 subjects: B.Sc., Ph.D., and Post- Doc
students in different areas of computer
science and education
• They installed the desktop application to
extract their user profiles, then ran all 7
variants of the described algorithms
• For each of the recommended tracks, the
users provide two different scores:
– how well the recommended track matches their
music preferences
– the novelty of the track
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
97. Образец заголовкаResults
• All Collaborative Filtering algorithms based
on tags (CFTG, CFTTI, CFTTN) performed
worse than the baseline, as standard User-
Item CF techniques already show high
precision
• All search algorithms show quite substantial
improvements over track based CF
• STG recommends much less popular tracks
than our CFTR baseline, but still of higher
quality
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
98. Образец заголовкаResults
• A first set of algorithms, using collaborative
filtering on tag profiles that were extracted from
tracks, proved to be less successful than the
baseline.
• A second set of tag-based search algorithms
however improved results’ quality significantly.
• In addition to a 44% increase in quality for the best
algorithm, search-based methods are also much
faster than collaborative filtering and do not suffer
from the cold start problem
Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu, The Benefit of Using Tag-Based Profiles
100. Образец заголовкаMotivation
• Enhance collaborative tagging systems to
meet some key challenges:
– community identification
– user and document recommendation
– ontology generation
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
101. Образец заголовкаCommunity Identification
• Existing community identification
techniques:
– Spectral: identify all major communities in a
large collection
– Bibliometrics: determine the pair-wise affinity
among users
– Network flow based: identify broader
communities containing a known existing
community
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
102. Образец заголовкаUser and Document Recommendation
• HITS (Kleinberg 1999) algorithm
• Experiment different link weighting
mechanisms and combinations with
hyperlink analysis to improve the
algorithm
• Pair-wise similarities between the given
document and the rest of the documents
• Pair-wise similarities between a given user
and the rest of the users
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
103. Образец заголовкаUser and Document Recommendation
• HITS (Kleinberg 1999) algorithm
• Experiment different link weighting
mechanisms and combinations with
hyperlink analysis to improve the
algorithm
• Pair-wise similarities between the given
document and the rest of the documents
• Pair-wise similarities between a given user
and the rest of the users
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
104. Образец заголовкаOntology Generation
• An ontology is one of the most efficient
structures for navigation
– any document can be reached with o(log(n))
• Hierarchical clustering problem
• Different clustering techniques use
different pair-wise similarity measures
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
105. Образец заголовкаOntology Generation Algorithm
1. identifies the set of documents for which the
hierarchy needs to be generated,
2. identifies all tags associated with these
documents.
3. constructs a document-tag matrix, denoted by A
– Aij = 1 iff document i is tagged by tag j
4. constructs a tag-tag matrix to store the semantic
similarities between tags
5. Multiplied A by the tag-tag matrix
6. Each document is now represented by a row
vector Ai
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
106. Образец заголовкаEvaluation
• Offline studies as pre-tests of the design
concepts
• Collect data through paper-based
questionnaires and face-to-face interviews
• Use test websites to evaluate selective
modules of the proposed design solutions
• Use pilot systems to evaluate the proposed
design in large knowledge creation
environments
• Simulate large amounts of user input data to
test the scalability
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
107. Образец заголовкаConclusions
• Collaborative tagging systems have the
potential of becoming a technological
infrastructure for harvesting social
knowledge
• There are many challenges
• The proposed designed prototypes
enhance social tagging systems to meet
some of the key challenges
• Preliminary results show promise
Harris Wu, Mohammad Zubair, Kurt Maly, Harvesting social knowledge from folksonomies
109. Образец заголовкаRecap
• Recommender systems are widely used in the web
– Facebook, Amazon, Netflix, …
• There are many different recommender algorithms
• Tradition recommender algorithms has pros and
cons
• Hybrid approaches combines multiple recommender
algorithms
• User profile is useful for personalized
recommendations
• Leveraging Tagging Systems with User Information
can improve results
110. Образец заголовкаTake-Aways
• Shared tags can improve resource discovery
• Using quadratic concepts of users, tags, resources and
profiles maximize sets of users sharing resources with
the same tags. They can be used to find a personalized
choice of tags and resources when suggestions are
made following the users’ profiles
• Hybrid tagging recommender system can cover more
dimensions of the data by different components
• Using tag-based search algorithms can significantly
improve the quality of results
• Collaborative tagging systems have many challenges,
but can be enhanced by using with other components
111. Образец заголовкаFuture Works
• Current project at work:
– There are a lot of files coming into the enterprise file
distribution system daily
– Files are tagged “automatically” based on file name and a
set of predefined rules
– Users subscribe to particular files based on predefined
subscriptions
• Problems:
– File name contains file metadata, so it must be a certain
format
– Difficult to manually manage all predefined rules and
subscriptions
– Some files might be useful for analysts, but they didn’t
subscribe
112. Образец заголовкаFuture Works
• Implement algorithm to automatically
suggest tags to a file
• Implement algorithm to recommend
public files to user based on their roles
and interests
113. Образец заголовкаAcknowledgements
• Daniar Asanov, Algortihms and Methods in Recommender Systems, 2011
• Robin Burke, Hybrid Recommender Systems: Survey and Experiments, User
Modeling and User-Adapted Interaction, v.12 n.4, p.331-370, November
2002
• Mohamed Nader Jelassi, Sadok Ben Yahia, Engelbert Mephu Ngui, A
Personalized Recommender System Based on Users’ Information In
Folksonomies, Proceedings of the 22nd International Conference on World
Wide Web, May 2013
• Kerstin Bischoff , Claudiu S. Firan , Wolfgang Nejdl , Raluca Paiu, Can all tags
be used for search?, Proceedings of the 17th ACM conference on
Information and knowledge management, October 26-30, 2008, Napa Valley,
California, USA
• Jonathan Gemmell , Thomas Schimoler , Bamshad Mobasher , Robin Burke,
Hybrid tag recommendation for social annotation systems, Proceedings of
the 19th ACM international conference on Information and knowledge
management, October 26-30, 2010, Toronto, ON, Canada
114. Образец заголовкаAcknowledgements
• Harris Wu , Mohammad Zubair , Kurt Maly, Harvesting social knowledge
from folksonomies, Proceedings of the seventeenth conference on Hypertext
and hypermedia, August 22-25, 2006, Odense, Denmark
• Hao Ma , Dengyong Zhou , Chao Liu , Michael R. Lyu , Irwin King,
Recommender systems with social regularization, Proceedings of the fourth
ACM international conference on Web search and data mining, February 09-
12, 2011, Hong Kong, China
• Philip Bonhard , Clare Harries , John McCarthy , M. Angela Sasse, Accounting
for taste: using profile similarity to improve recommender systems,
Proceedings of the SIGCHI Conference on Human Factors in Computing
Systems, April 22-27, 2006, Montréal, Québec, Canada
• Claudiu S. Firan , Wolfgang Nejdl , Raluca Paiu, The Benefit of Using Tag-
Based Profiles, Proceedings of the 2007 Latin American Web Conference,
p.32-41, October 31-November 02, 2007
• Mohsen Jamali , Martin Ester, A matrix factorization technique with trust
propagation for recommendation in social networks, Proceedings of the
fourth ACM conference on Recommender systems, September 26-30, 2010,
Barcelona, Spain
User-based:
Recommendations are given to user based on evaluation of items by other users sharing common preferences
Item-based:
Predictions are calculated based on the similarity of ratings given by users for the items
More apt for offline preprocessing of large rating matrix
This approach predicts the relevance of items for users based on user history, such as items previously purchased, viewed or liked by the visitor. The system compares one user’s history to others’ user journeys and based on this data, it creates a list of recommended items for the user. The collaborative filtering method suffers from the cold start problem, meaning that it cannot recommend items without historical data. They can be further classified into model-based and memory-based algorithms.
User-based:
Recommendations are given to user based on evaluation of items by other users sharing common preferences
Item-based:
Predictions are calculated based on the similarity of ratings given by users for the items
More apt for offline preprocessing of large rating matrix
Content-based filtering (CBF) algorithms recommend items whose metadata are similar to the metadata of items the user has interacted with in the past. For instance, in the case of product recommendations, the product description, category, price, physical parameters, etc. are content metadata. Unlike the collaborative filtering approach, CBF does not suffer from new-item and cold-start problems.
Goal: recommend items similar to those the user liked