The rapid evolution of deep learning technologies and the explosion of diverse user interaction traces have brought significant challenges and opportunities to recommendation and personalized systems. In this workshop, we discussed recent trends and techniques in user modeling and presented our work on immersive recommendation systems. These systems learn users’ preferences from diverse digital trace modalities (text, image and unstructured data streams) in a wide range of recommendation domains (creative art, food, news, and events). The workshop included a light tutorial on OpenRec, an open source framework that enables quick prototyping of complex recommender systems via modularization.
This workshop is based on research and development done at Cornell Tech as part of the Connected Experiences Lab, supported by Oath and NSF.
This presentation was provided by Dr. Micah Altman of MIT during the NISO Symposium, Privacy Implications of Research Data, held on September 11, 2016 in conjunction with the International Data Week events in Denver, Colorado.
Writing Analytics for Epistemic Features of Student Writing #icls2016 talkSimon Knight
Talk presented at #ICLS2016 presented in Singapore. I discuss levels of description as sites of epistemic cognition focusing on writing and use of textual features to associate rubric scores with epistemic cognition.
My thanks to my collaborators (listed on the paper) particularly Laura Allen, who also generously let me adapt the later slides on NLP studies of writing.
Abstract: Literacy, encompassing the ability to produce written outputs from the reading of multiple sources, is a key learning goal. Selecting information, and evaluating and integrating claims from potentially competing documents is a complex literacy task. Prior research exploring differing behaviours and their association to constructs such as epistemic cognition has used ‘multiple document processing’ (MDP) tasks. Using this model, 270 paired participants, wrote a review of a document. Reports were assessed using a rubric associated with features of complex literacy behaviours. This paper focuses on the conceptual and empirical associations between those rubric-marks and textual features of the reports on a set of natural language processing (NLP) indicators. Findings indicate the potential of NLP indicators for providing feedback regarding the writing of such outputs, demonstrating clear relationships both across rubric facets and between rubric facets and specific NLP indicators.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Panel: Our Scholarly Recognition System Doesn’t Still WorkDaniel S. Katz
A panel at the 2015 Science of Team Science (SciTS) Conference
Organizers: Daniel S. Katz (U. of Chicago & Argonne National Laboratory), Amy Brand (Digital Science), Melissa Haendel (Oregon Health & Science University), Holly J. Falk-Krzesinski (Elsevier)
Panelists: Robin Champieux (Oregon Health & Science University) Holly Falk-Krzesinski (Elsevier)Daniel S. Katz (U. of Chicago & Argonne National Laboratory)Philippa Saunders (University of Edinburgh)
Abstract: http://bit.ly/scholarly-recognition
Epistemic networks for Epistemic CommitmentsSimon Knight
The ways in which people seek and process information are fundamentally epistemic in nature. Existing epistemic cognition research has tended towards characterizing this fundamental relationship as cognitive or belief-based in nature. This paper builds on recent calls for a shift towards activity-oriented perspectives on epistemic cognition and proposes a new theory of ‘epistemic commitments’. An additional contribution of this paper comes from an analytic approach to this recast construct of epistemic commitments through the use of Epistemic Network Analysis (ENA) to explore connections between particular modes of epistemic commitment. Illustrative examples are drawn from existing research data on children’s epistemic talk when engaged in collaborative information seeking tasks. A brief description of earlier analysis of this data is given alongside a newly conducted ENA to demonstrate the potential for such an approach.
Paper at: http://oro.open.ac.uk/39254/
This presentation was provided by Dr. Micah Altman of MIT during the NISO Symposium, Privacy Implications of Research Data, held on September 11, 2016 in conjunction with the International Data Week events in Denver, Colorado.
Writing Analytics for Epistemic Features of Student Writing #icls2016 talkSimon Knight
Talk presented at #ICLS2016 presented in Singapore. I discuss levels of description as sites of epistemic cognition focusing on writing and use of textual features to associate rubric scores with epistemic cognition.
My thanks to my collaborators (listed on the paper) particularly Laura Allen, who also generously let me adapt the later slides on NLP studies of writing.
Abstract: Literacy, encompassing the ability to produce written outputs from the reading of multiple sources, is a key learning goal. Selecting information, and evaluating and integrating claims from potentially competing documents is a complex literacy task. Prior research exploring differing behaviours and their association to constructs such as epistemic cognition has used ‘multiple document processing’ (MDP) tasks. Using this model, 270 paired participants, wrote a review of a document. Reports were assessed using a rubric associated with features of complex literacy behaviours. This paper focuses on the conceptual and empirical associations between those rubric-marks and textual features of the reports on a set of natural language processing (NLP) indicators. Findings indicate the potential of NLP indicators for providing feedback regarding the writing of such outputs, demonstrating clear relationships both across rubric facets and between rubric facets and specific NLP indicators.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Panel: Our Scholarly Recognition System Doesn’t Still WorkDaniel S. Katz
A panel at the 2015 Science of Team Science (SciTS) Conference
Organizers: Daniel S. Katz (U. of Chicago & Argonne National Laboratory), Amy Brand (Digital Science), Melissa Haendel (Oregon Health & Science University), Holly J. Falk-Krzesinski (Elsevier)
Panelists: Robin Champieux (Oregon Health & Science University) Holly Falk-Krzesinski (Elsevier)Daniel S. Katz (U. of Chicago & Argonne National Laboratory)Philippa Saunders (University of Edinburgh)
Abstract: http://bit.ly/scholarly-recognition
Epistemic networks for Epistemic CommitmentsSimon Knight
The ways in which people seek and process information are fundamentally epistemic in nature. Existing epistemic cognition research has tended towards characterizing this fundamental relationship as cognitive or belief-based in nature. This paper builds on recent calls for a shift towards activity-oriented perspectives on epistemic cognition and proposes a new theory of ‘epistemic commitments’. An additional contribution of this paper comes from an analytic approach to this recast construct of epistemic commitments through the use of Epistemic Network Analysis (ENA) to explore connections between particular modes of epistemic commitment. Illustrative examples are drawn from existing research data on children’s epistemic talk when engaged in collaborative information seeking tasks. A brief description of earlier analysis of this data is given alongside a newly conducted ENA to demonstrate the potential for such an approach.
Paper at: http://oro.open.ac.uk/39254/
Presentation by Allen Flynn, Johmarx Patton, and Jodyn Platt at the 48th Annual at the Hawaii International Conference on System Sciences (HICSS) in January 2015 (http://kholden7.wix.com/hicss).
Flynn, Patton, and Platt were all core member of Learning Health System Third Century Initiative Phase 1 Project and continue to participate in other Learning Health System Initiatives led by the Department of Learning Health Sciences.
How to find out about the usability of your web site using a survey by @cjformsCaroline Jarrett
Workshop at UX Cambridge 2012 led by Caroline Jarrett. We agreed that you can't do a usability test using a questionnaire alone, but that you can find out things about usability using surveys.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
Slides from presentation at CHI2015:
Paper Title: Designing for Citizen Data Analysis: A Cross-Sectional Case Study of a Multi-Domain Citizen Science Platform
Abstract:
Designing an effective and sustainable citizen science (CS) project requires consideration of a great number of factors. This makes the overall process unpredictable, even when a sound, user-centred design approach is followed by an experienced team of UX designers. Moreover, when such systems are deployed, the complexity of the resulting interactions challenges any attempt to generalisation from retrospective analysis. In this paper, we present a case study of the largest single platform of citizen driven data analysis projects to date, the Zooniverse. By eliciting, through structured reflection, experiences of core members of its design team, our grounded analysis yielded four sets of themes, focusing on Task Specificity, Community Development, Task Design and Public Relations and Engagement. For each, we propose a set of design claims (DCs), drawing comparisons to the literature on crowdsourcing and online communities to contextualise our findings.
Student Achievement Review (initially presented during Inauguration Function of the Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)) - updated since
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
Presentation by Allen Flynn, Johmarx Patton, and Jodyn Platt at the 48th Annual at the Hawaii International Conference on System Sciences (HICSS) in January 2015 (http://kholden7.wix.com/hicss).
Flynn, Patton, and Platt were all core member of Learning Health System Third Century Initiative Phase 1 Project and continue to participate in other Learning Health System Initiatives led by the Department of Learning Health Sciences.
How to find out about the usability of your web site using a survey by @cjformsCaroline Jarrett
Workshop at UX Cambridge 2012 led by Caroline Jarrett. We agreed that you can't do a usability test using a questionnaire alone, but that you can find out things about usability using surveys.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
Slides from presentation at CHI2015:
Paper Title: Designing for Citizen Data Analysis: A Cross-Sectional Case Study of a Multi-Domain Citizen Science Platform
Abstract:
Designing an effective and sustainable citizen science (CS) project requires consideration of a great number of factors. This makes the overall process unpredictable, even when a sound, user-centred design approach is followed by an experienced team of UX designers. Moreover, when such systems are deployed, the complexity of the resulting interactions challenges any attempt to generalisation from retrospective analysis. In this paper, we present a case study of the largest single platform of citizen driven data analysis projects to date, the Zooniverse. By eliciting, through structured reflection, experiences of core members of its design team, our grounded analysis yielded four sets of themes, focusing on Task Specificity, Community Development, Task Design and Public Relations and Engagement. For each, we propose a set of design claims (DCs), drawing comparisons to the literature on crowdsourcing and online communities to contextualise our findings.
Student Achievement Review (initially presented during Inauguration Function of the Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)) - updated since
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
A presentation given by
Daphne Duin and co-authored with David Self, Simon Rycroft, Dave Roberts & Vincent Smith at the EDIT general meeting, Carvoeiro, Portugal. Dec. 15-17, 2009.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Two Brains are Better than One: User Control in Adaptive Information AccessPeter Brusilovsky
In recent years, the use of Artificial Intelligence (AI) technologies expanded to many areas where they directly affect the lives of many people. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a patient from a hospital and whether a specific student is at risk to fail a course. Such an extensive use in AI in decision making came with a range of protentional problems that have been extensively studied over the last few years. Recognition of these problems motivated a rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies. Among the ideas of human-centered AI is user control - engaging users in affecting AI decision making to prevent possible errors and biases. In my talk, I will focus on the application of user control in one popular area of AI application, adaptive information access. Adaptive information access systems such as personalized search and recommender systems attempt to model their users to help them in finding the most relevant information. Yet, user modeling and personalization mechanisms might not always work as expected resulting in errors, biases, and suboptimal behavior. Combining the decision power or AI with the ability of the user to guide and control it brings together the strong sides of artificial and human intelligence and could lead to better results. In my talk, I review several projects focused on user control in adaptive information access systems and discuss the benefits and challenges of this approach.
David Leeming 67 BricksAI and machine learning has been generating a lot of attention over the past couple of years, but they still raise a lot of questions for our industry. How should publishers, librarians and researchers engage with these technologies? Are these technologies a threat to the current scholarly ecosystem or an opportunity? Can these technologies really help us drive the discovery and dissemination of research? How have these technologies already become an essential part of the scholarly ecosystem? After a short introduction to the concepts of AI and machine learning we will address these questions by engaging the audience in a live interactive demonstration in which we work together to train a machine learning algorithm to work with scholarly content. We will share areas of opportunity we have uncovered from our experiences of working with these technologies within the industry and discuss how publishers, librarians and researchers might work with these technologies to further advance the future of scholarly communication.
2016 07 12_purdue_bigdatainomics_seandavisSean Davis
Newer, faster, cheaper molecular assays are driving biomedical research. I discuss the history of biomedical data including concepts of data sharing, hypothesis-driven vs generating research, and the potential to expand our thinking on biomedical research to be much more integrated through smart, creative, and open use of technologies and more flexible, longitudinal studies.
ML practitioners and advocates are increasingly finding themselves becoming gatekeepers of the modern world. The models you create have power to get people arrested or vindicated, get loans approved or rejected, determine what interest rate should be charged for such loans, who should be shown to you in your long list of pursuits on your Tinder, what news do you read, who gets called for a job phone screen or even a college admission... the list goes on. My goal in this talk is to summarize the kinds of disparate outcomes that are caused by cargo cult machine learning, and recent academic efforts to address some of them.
Univ of Miami CTSI: Citizen science seminar; Oct 2014Richard Bookman
The University of Miami's Clinical & Translational Science Institute runs a seminar course for MS students.
This talk surveys 8 citizen science projects, reviews NIH's current activities, and identifies issues for attention, particularly with ethical, legal and social implications.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
ODSC East 2017: Data Science Models For GoodKarry Lu
Abstract: The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immersive Recommendation Workshop, NYC Media Lab'17
1. Immersive Recommendation
Deep User and Content Modeling for Personalization
Longqi Yang, Ph.D. student
Connected Experiences Lab, Small Data Lab
Cornell Tech
12. Netflix Challenge (Prize)
We’re quite curious, really. To the tune of one million dollars…
… To help customers find movies, we’ve developed our world-class movie
recommendation system: Cinematch. Its job is to predict whether someone will enjoy a
movie based on how much they liked or disliked other movies …
… We provide you with a lot of anonymous rating data, and a prediction accuracy bar that
is 10% better than what Cinematch can do on the same training data set… If you develop a
system that we judge most beats that bar on the qualifying test set we provide, you get
serious money and the bragging rights …
23. One Example – Bayesian Personalized Ranking (BPR)
𝒖𝒊, 𝒗 𝒑, 𝒗 𝒏For all
m𝑎𝑥 ln 𝜎 𝒖𝒊 ∙ 𝒗 𝒑 − 𝒖𝒊 ∙ 𝒗 𝒏
Rendle, Steffen, et al. "BPR: Bayesian personalized ranking from
implicit feedback." Proceedings of the twenty-fifth conference on
uncertainty in artificial intelligence. AUAI Press, 2009.
user i
An item that the user “click”
An item that the user does not “click”
24. Algorithms (Incomprehensive List)
• Weighted Regularized Matrix Factorization (WRMF)
• Probabilistic Matrix Factorization (PMF)
“Shallow” Models:
• Weighted Approximately Ranked Pairwise Loss (WARP)
“Deep” Models:
Hsieh, Cheng-Kang, et al. "Collaborative metric learning." Proceedings of the 26th International
Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017.• Collaborative Metric Learning (CML)
He, Xiangnan, et al. "Neural collaborative filtering." Proceedings of the 26th International Conference
on World Wide Web. International World Wide Web Conferences Steering Committee, 2017.• Neural Collaborative Filtering
Mnih, Andriy, and Ruslan R. Salakhutdinov. "Probabilistic matrix factorization." Advances in
neural information processing systems. 2008.
• Wide and Deep Learning for Recommender Systems
Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback
datasets." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 2008.
Cheng, Heng-Tze, et al. "Wide & deep learning for recommender systems." Proceedings of the 1st
Workshop on Deep Learning for Recommender Systems. ACM, 2016.
Weston, Jason, Samy Bengio, and Nicolas Usunier. "Wsabie: Scaling up to large vocabulary image
annotation." IJCAI. Vol. 11. 2011.
25. Beyond Matrix
Implicit feedback
Deep Content Modeling
Beyond “black-box items”
Beyond Accuracy
Diversity and Fairness
Rich Context and Modality
Learning preference from auxiliary channels
26. Pure Collaborative Filtering is cool (and maybe accurate), but real world
recommendations are far more complex than “likes”
Understanding the contents really matters! - Articles
Is it appropriate to recommend
these two articles together?
27. Pure Collaborative Filtering is cool (and maybe accurate), but real world
recommendations are far more complex than “likes”
Understanding the contents really matters! - Food
Random
(Most healthy)
Trattner, Christoph, and David Elsweiler.
"Investigating the healthiness of internet-
sourced recipes: implications for meal
planning and recommender systems."
Proceedings of the 26th International
Conference on World Wide Web. International
World Wide Web Conferences Steering
Committee, 2017
28. Pure Collaborative Filtering is cool (and maybe accurate), but real world
recommendations are far more complex than “likes”
Understanding the contents really matters! - Food
“Users in general tend to interact most often with the least
healthy recipes. Recommender algorithms tend to score
popular items highly and thus on average promote unhealthy
items.”
Trattner, Christoph, and David Elsweiler.
"Investigating the healthiness of internet-
sourced recipes: implications for meal
planning and recommender systems."
Proceedings of the 26th International
Conference on World Wide Web. International
World Wide Web Conferences Steering
Committee, 2017
29. Pure Collaborative Filtering is cool (and maybe accurate), but real world
recommendations are far more complex than “likes”
Understanding the contents really matters! – Cold Start
A new fashion cloth
A new online course
A new job post
Recommendations
without user feedback
30. Deep Content Modeling for Recommendations
(C/R/Res/Adversarial/Rei
nforcement) NN
User Item Interaction
Optimization
31. Beyond Matrix
Implicit feedback
Deep Content Modeling
Beyond “black-box items”
Beyond Accuracy
Diversity and Fairness
Rich Context and Modality
Learning preference from auxiliary channels
32. Are ratings/clicks/views enough for recommendations?
Context matters! – Music Recommendation
Schedl, Markus, et al. "Music recommender systems."
Recommender Systems Handbook. Springer US, 2015. 453-492.
Schedl, Markus, Peter Knees, and Fabien Gouyon. "New Paths in
Music Recommender Systems Research." Proceedings of the
Eleventh ACM Conference on Recommender Systems. ACM, 2017.
time location weather
Environmental Context
Individual Context
emotion activity social context
Schedl, Markus, Georg Breitschopf, and Bogdan Ionescu.
"Mobile Music Genius: Reggae at the Beach, Metal on a
Friday Night?." Proceedings of International Conference on
Multimedia Retrieval. ACM, 2014.
33. Recommendations are not always “a list”: Rich modality
Sun, Yu, et al. "Contextual intent tracking for personal assistants."
Proceedings of the 22nd ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining. ACM, 2016.
Kang, Jie, et al. "Understanding How People Use Natural
Language to Ask for Recommendations." Proceedings of the
Eleventh ACM Conference on Recommender Systems. ACM, 2017.
34. Rich Context and Modality
(C/R/Res/Adversarial/Rei
nforcement) NN
User Item Interaction
Optimization
(C/R/Res/Adversarial/Rei
nforcement) NN
(C/R/Res/Adversarial/Rei
nforcement) NN
35. Beyond Matrix
Implicit feedback
Deep Content Modeling
Beyond “black-box items”
Beyond Accuracy
Diversity and Fairness
Rich Context and Modality
Learning preference from auxiliary channels
38. Fairness – Long tail and Minority
# views
(attention)
popular unpopular
39. Fairness – Long tail and Minority
Recommender system
better
worse
Yao, Sirui, and Bert Huang. "Beyond Parity: Fairness
Objectives for Collaborative Filtering." arXiv preprint
arXiv:1705.08804 (2017).
40. Incorporating diversity and fairness into recommendations
(C/R/Res/Adversarial/Rei
nforcement) NN
User Item Interaction
Optimization
(C/R/Res/Adversarial/Rei
nforcement) NN
(C/R/Res/Adversarial/Rei
nforcement) NN
Penalize homogeneous and
unfair recommendations
44. Yum-me
Bringing healthiness into the recommendation of food
Yang, Longqi, et al. "Yum-Me: A Personalized Nutrient-Based Meal Recommender
System." ACM Transactions on Information Systems (TOIS) 36.1 (2017): 7.
45. *Number of Americans Living with Diet-and Inactivity-Related Diseases
Obesity
HBP
Diabetes
113M
50M
15M
Critical Issue of Food
46. The problem is not awareness, but adherence
How can we (efficiently) find meals that are healthy but also
cater to people’s tastes? - Bringing the notion of healthiness into
recommendations!
47. Yum-me: An interactive healthy meal recommendation system
Take a look at the food below and tap all
that look delicious to you.
http:// http://
Compare the food pair below and tap on
whichever looks delicious to you.
Press on Yuck if neither of
them fits to your taste
2iters + 13iters
2iters + 13iters
2iters + 13iters
Browser
Mobile
Wearable
Personal Dietary Profile
(Food Preferences)
… …
Healthy meal recommendations based
on dietary restrictions
Re-ranking
Personalized healthy meal recommendations
…...
…...
Phase I Phase II
Take a look at the food
below and tap all that
look delicious to you.
Compare the food pair below
and tap on whichever looks
delicious to you.
Press on Yuck if neither of
them fits to your taste
Take a look at the food
below and tap all that
look delicious to you.
Compare the food pair below
and tap on whichever looks
delicious to you.
Press on Yuck if neither of
them fits to your taste
http://
Choose the closest diet type to you.
⌾No restrictions ⌾ Vegetarian⌾ Vegan
⌾ Kosher ⌾ Halal
Identify your health goals.
⌾Reduce ⌾ Maintain ⌾ Increase
⌾Reduce ⌾ Maintain ⌾ Increase
⌾Reduce ⌾ Maintain ⌾ Increase
Calories
Protein
Fat
+
Survey
Choose the closest diet
type to you.
Identify your health goals.
⌾Reduce
⌾ Maintain
Calories
⌾No restrictions
⌾ Vegetarian
⌾ Vegan
⌾ Kosher
⌾ Halal
+
+
Choose the closest diet
type to you.
⌾No restrictions
⌾ Vegetarian
⌾ Vegan
⌾ Kosher
⌾ Halal
48. Yum-me: An interactive healthy meal recommendation system
Take a look at the food below and tap all
that look delicious to you.
http:// http://
Compare the food pair below and tap on
whichever looks delicious to you.
Press on Yuck if neither of
them fits to your taste
2iters + 13iters
2iters + 13iters
2iters + 13iters
Browser
Mobile
Wearable
Personal Dietary Profile
(Food Preferences)
… …
Healthy meal recommendations based
on dietary restrictions
Re-ranking
Personalized healthy meal recommendations
…...
…...
Phase I Phase II
Take a look at the food
below and tap all that
look delicious to you.
Compare the food pair below
and tap on whichever looks
delicious to you.
Press on Yuck if neither of
them fits to your taste
Take a look at the food
below and tap all that
look delicious to you.
Compare the food pair below
and tap on whichever looks
delicious to you.
Press on Yuck if neither of
them fits to your taste
http://
Choose the closest diet type to you.
⌾No restrictions ⌾ Vegetarian⌾ Vegan
⌾ Kosher ⌾ Halal
Identify your health goals.
⌾Reduce ⌾ Maintain ⌾ Increase
⌾Reduce ⌾ Maintain ⌾ Increase
⌾Reduce ⌾ Maintain ⌾ Increase
Calories
Protein
Fat
+
Survey
Choose the closest diet
type to you.
Identify your health goals.
⌾Reduce
⌾ Maintain
Calories
⌾No restrictions
⌾ Vegetarian
⌾ Vegan
⌾ Kosher
⌾ Halal
+
+
Choose the closest diet
type to you.
⌾No restrictions
⌾ Vegetarian
⌾ Vegan
⌾ Kosher
⌾ Halal
53. Yum-me: An interactive healthy meal recommendation system
Take a look at the food below and tap all
that look delicious to you.
http:// http://
Compare the food pair below and tap on
whichever looks delicious to you.
Press on Yuck if neither of
them fits to your taste
2iters + 13iters
2iters + 13iters
2iters + 13iters
Browser
Mobile
Wearable
Personal Dietary Profile
(Food Preferences)
… …
Healthy meal recommendations based
on dietary restrictions
Re-ranking
Personalized healthy meal recommendations
…...
…...
Phase I Phase II
Take a look at the food
below and tap all that
look delicious to you.
Compare the food pair below
and tap on whichever looks
delicious to you.
Press on Yuck if neither of
them fits to your taste
Take a look at the food
below and tap all that
look delicious to you.
Compare the food pair below
and tap on whichever looks
delicious to you.
Press on Yuck if neither of
them fits to your taste
http://
Choose the closest diet type to you.
⌾No restrictions ⌾ Vegetarian⌾ Vegan
⌾ Kosher ⌾ Halal
Identify your health goals.
⌾Reduce ⌾ Maintain ⌾ Increase
⌾Reduce ⌾ Maintain ⌾ Increase
⌾Reduce ⌾ Maintain ⌾ Increase
Calories
Protein
Fat
+
Survey
Choose the closest diet
type to you.
Identify your health goals.
⌾Reduce
⌾ Maintain
Calories
⌾No restrictions
⌾ Vegetarian
⌾ Vegan
⌾ Kosher
⌾ Halal
+
+
Choose the closest diet
type to you.
⌾No restrictions
⌾ Vegetarian
⌾ Vegan
⌾ Kosher
⌾ Halal
55. User study
Step 1. Users identify their
diet types and health goals.
Step 2. Users use visual
interace to express their
fine-grained food
preferences.
Step 3. Users identify each of
recommended meals as either
Yummy or No way. (The order
of the items is randomized)
Top 500 healthy items that
meet users’ diet types and
health goals.
Select top 10 items
ranked by user’s
fine-grained dietary
preference.
Randomly select 10
food items from 500
healthy meal pool.
…... …...
…...…...
57. User study
Goal: reduce calories (25 users) Goal: maintain calories (21 users)
Goal: maintain protein (36 users) Goal: increase protein (12 users) Goal: reduce fat (17 users)
Goal: increase calories (2 users)
Goal: maintain fat (30 users)
users’ 20 favorite meals
meals recommended by Yum-me
and accepted by users.
Averageamountofnutrients
perserving(normalized)
Averageamountofnutrients
perserving(normalized)
Averageamountofnutrients
perserving(normalized)
58. Creative Content Recommendation
Bringing unstructured command traces into the recommendation of art
Yang, Longqi, et al. "Personalizing Software and Web Services by
Integrating Unstructured Application Usage Traces." Proceedings of the
26th International Conference on World Wide Web Companion. International
World Wide Web Conferences Steering Committee, 2017.
59. Cold-start creative content recommendation
Day to day work activities
(Commands performed in
professional design software)
72. OpenRec: Experimentation and innovation through Extension
rating
item text item textitem image
rating
user text
item image
item text
rating
rating
user
text
user
demogr
item
text
item
image
integrator
module
extractor
module
interaction
module
R1 R2
R4R3
73. OpenRec: Architecture
Module
Extractor IntegratorInteraction
BPR WARP
PMF CML
NeuMF …
LF
ResNet MLP
LSTM
FoodDist
Concatenation
Average
Weighted sum
……
Recommender
News recommender
system with users’ click
history, Twitter posts
and news topic modeling.
Music recommender system
with users’ listen history,
lyrics and audio analysis
…
Utility
Sampler
Pairwise
sampler
Triplet
sampler
…
Evaluator
…
AUC
Recall@K