Recommendation system using unsupervised machine learning algorithm & associjerd
This document discusses using a combination of unsupervised machine learning algorithms, including Farthest First clustering and the Apriori association rule algorithm, for a course recommendation system. It presents an approach that clusters student data from a learning management system (LMS) like Moodle without needing to preprocess the data. Then, association rules are generated to find the best combinations of courses based on the student clusters. The combined approach is tested on sample LMS data to demonstrate its ability to recommend courses without requiring data preparation steps compared to using only the Apriori algorithm.
In this presentation I will talk about the design of scalable recommender systems and its similarity with advertising systems. The problem of generating and delivering recommendations of content/products to appropriate audiences and ultimately to individual users at scale is largely similar to the matching problem in computational advertising, specially in the context of dealing with self and cross promotional content. In this analogy with online advertising a display opportunity triggers a recommendation. The actors are the publisher (website/medium/app owner) the advertiser (content owner or promoter), whereas the ads or creatives represent the items being recommended that compete for the display opportunity and may have different monetary value to the actors. To effectively control what is recommended to whom, targeting constraints need to be defined over an attribute space, typically grouped by type (Audience, Content, Context, etc.) where some associated values are not known until decisioning time. In addition to constraints, there are business objectives (e.g. delivery quota) defined by the actors. Both constraints and objectives can be encapsulated into and expressed as campaigns. Finally, there there is the concept of relevance, directly related to users' response prediction that is computed using the same attribute space used as signals.
As in advertising, recommendation systems require a serving platform where decisioning happens in real-time (few milliseconds) typically selecting an optimal set of items to display to the user from hundreds, sometimes thousands or millions of items. User actions are then taken as feedback and used to learn models that dynamically adjust order to meet business objectives.
This is a radical departure from the traditional item-based and user-based collaborative filtering approach to recommender systems, which fails to factor-in context, such as time-of-day, geo-location or category of the surrounding content to generate more accurate recommendations. Traditional approaches also fail to recognize that recommendations don't happen in a vacuum and as such may require the evaluation of business constraints and objectives. All this should be considered when designing and developing true commercial recommender/advertising systems.
Speaker Bio
Joaquin A. Delgado is currently Director of Advertising Technology at Intel Media (a wholly owned subsidiary of Intel Corp.), working on disruptive technologies in the Internet T.V. space. Previous to that he held CTO positions at AdBrite, Lending Club and TripleHop Technologies (acquired by Oracle). He was also Director of Engineering and Sr. Architect Principal at Yahoo! His expertise lies on distributed systems, advertising technology, machine learning, recommender systems and search. He holds a Ph.D in computer science and artificial intelligence from Nagoya Institute of Technology, Japan.
Introduction and new trends in Recommender SystemsPaolo Tomeo
This document provides an introduction to recommender systems, including common approaches like content-based filtering, collaborative filtering, and hybrid approaches. It discusses strengths and drawbacks of different methods and key aspects of evaluating recommender systems like accuracy, diversity, novelty and explanation. Emerging trends mentioned include deep learning, wide and deep learning, multi-criteria recommendations, graph-based algorithms, and using semantic web data.
Content - Based Recommendations Enhanced with Collaborative InformationAlessandro Liparoti
This document discusses content-based recommender systems and describes a content-based collaborative (CBC) hybrid approach. CBC builds a content-based model enhanced with collaborative data by using a content-based similarity function that incorporates user-feature weights and feature importance weights. The CBC method is evaluated on movie recommendation datasets and is shown to outperform collaborative and content-based baselines, especially in new-item recommendation scenarios where no ratings exist.
Recsys 2014 Tutorial - The Recommender Problem RevisitedXavier Amatriain
This document summarizes Xavier Amatriain's presentation on recommender systems. It discusses traditional recommendation methods like collaborative filtering, content-based recommendations, and hybrid approaches. It also covers newer methods that go beyond traditional techniques, such as learning to rank, deep learning, social recommendations, and context-aware recommendations. Throughout the presentation, Amatriain discusses challenges like cold starts, popularity bias, and limitations of different recommendation approaches. He also shares lessons learned from the Netflix Prize competition, including how SVD and RBM models were used.
Kdd 2014 Tutorial - the recommender problem revisitedXavier Amatriain
This document summarizes a presentation given by Xavier Amatriain at the KDD conference in August 2014 about recommender systems. The presentation discusses the evolution of the recommender problem from search to discovery and recommendation. It then covers traditional recommendation methods like collaborative filtering, both memory-based and model-based using matrix factorization. It discusses challenges like sparsity and explores techniques learned from the Netflix Prize like SVD, restricted Boltzmann machines, and ensemble methods.
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Xavier Amatriain
The document summarizes a presentation on recommender systems given by Xavier Amatriain. It begins with introductions to recommender systems and collaborative filtering. Traditional collaborative filtering approaches include user-based and item-based methods. User-based CF finds similar users to a target user and recommends items they liked. Item-based CF finds similar items to those a target user liked and predicts ratings. Both approaches address sparsity and scalability challenges with dimensionality reduction techniques.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
This document discusses modern perspectives on recommender systems and their applications at Mendeley. It covers the evolution of recommender problems from rating prediction to context-aware recommendations. It also discusses common recommender algorithms like collaborative filtering, content-based filtering and hybrid approaches. The document concludes by discussing how Mendeley uses recommenders for related research, researchers to follow, and other use cases.
Recommendation system using unsupervised machine learning algorithm & associjerd
This document discusses using a combination of unsupervised machine learning algorithms, including Farthest First clustering and the Apriori association rule algorithm, for a course recommendation system. It presents an approach that clusters student data from a learning management system (LMS) like Moodle without needing to preprocess the data. Then, association rules are generated to find the best combinations of courses based on the student clusters. The combined approach is tested on sample LMS data to demonstrate its ability to recommend courses without requiring data preparation steps compared to using only the Apriori algorithm.
In this presentation I will talk about the design of scalable recommender systems and its similarity with advertising systems. The problem of generating and delivering recommendations of content/products to appropriate audiences and ultimately to individual users at scale is largely similar to the matching problem in computational advertising, specially in the context of dealing with self and cross promotional content. In this analogy with online advertising a display opportunity triggers a recommendation. The actors are the publisher (website/medium/app owner) the advertiser (content owner or promoter), whereas the ads or creatives represent the items being recommended that compete for the display opportunity and may have different monetary value to the actors. To effectively control what is recommended to whom, targeting constraints need to be defined over an attribute space, typically grouped by type (Audience, Content, Context, etc.) where some associated values are not known until decisioning time. In addition to constraints, there are business objectives (e.g. delivery quota) defined by the actors. Both constraints and objectives can be encapsulated into and expressed as campaigns. Finally, there there is the concept of relevance, directly related to users' response prediction that is computed using the same attribute space used as signals.
As in advertising, recommendation systems require a serving platform where decisioning happens in real-time (few milliseconds) typically selecting an optimal set of items to display to the user from hundreds, sometimes thousands or millions of items. User actions are then taken as feedback and used to learn models that dynamically adjust order to meet business objectives.
This is a radical departure from the traditional item-based and user-based collaborative filtering approach to recommender systems, which fails to factor-in context, such as time-of-day, geo-location or category of the surrounding content to generate more accurate recommendations. Traditional approaches also fail to recognize that recommendations don't happen in a vacuum and as such may require the evaluation of business constraints and objectives. All this should be considered when designing and developing true commercial recommender/advertising systems.
Speaker Bio
Joaquin A. Delgado is currently Director of Advertising Technology at Intel Media (a wholly owned subsidiary of Intel Corp.), working on disruptive technologies in the Internet T.V. space. Previous to that he held CTO positions at AdBrite, Lending Club and TripleHop Technologies (acquired by Oracle). He was also Director of Engineering and Sr. Architect Principal at Yahoo! His expertise lies on distributed systems, advertising technology, machine learning, recommender systems and search. He holds a Ph.D in computer science and artificial intelligence from Nagoya Institute of Technology, Japan.
Introduction and new trends in Recommender SystemsPaolo Tomeo
This document provides an introduction to recommender systems, including common approaches like content-based filtering, collaborative filtering, and hybrid approaches. It discusses strengths and drawbacks of different methods and key aspects of evaluating recommender systems like accuracy, diversity, novelty and explanation. Emerging trends mentioned include deep learning, wide and deep learning, multi-criteria recommendations, graph-based algorithms, and using semantic web data.
Content - Based Recommendations Enhanced with Collaborative InformationAlessandro Liparoti
This document discusses content-based recommender systems and describes a content-based collaborative (CBC) hybrid approach. CBC builds a content-based model enhanced with collaborative data by using a content-based similarity function that incorporates user-feature weights and feature importance weights. The CBC method is evaluated on movie recommendation datasets and is shown to outperform collaborative and content-based baselines, especially in new-item recommendation scenarios where no ratings exist.
Recsys 2014 Tutorial - The Recommender Problem RevisitedXavier Amatriain
This document summarizes Xavier Amatriain's presentation on recommender systems. It discusses traditional recommendation methods like collaborative filtering, content-based recommendations, and hybrid approaches. It also covers newer methods that go beyond traditional techniques, such as learning to rank, deep learning, social recommendations, and context-aware recommendations. Throughout the presentation, Amatriain discusses challenges like cold starts, popularity bias, and limitations of different recommendation approaches. He also shares lessons learned from the Netflix Prize competition, including how SVD and RBM models were used.
Kdd 2014 Tutorial - the recommender problem revisitedXavier Amatriain
This document summarizes a presentation given by Xavier Amatriain at the KDD conference in August 2014 about recommender systems. The presentation discusses the evolution of the recommender problem from search to discovery and recommendation. It then covers traditional recommendation methods like collaborative filtering, both memory-based and model-based using matrix factorization. It discusses challenges like sparsity and explores techniques learned from the Netflix Prize like SVD, restricted Boltzmann machines, and ensemble methods.
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Xavier Amatriain
The document summarizes a presentation on recommender systems given by Xavier Amatriain. It begins with introductions to recommender systems and collaborative filtering. Traditional collaborative filtering approaches include user-based and item-based methods. User-based CF finds similar users to a target user and recommends items they liked. Item-based CF finds similar items to those a target user liked and predicts ratings. Both approaches address sparsity and scalability challenges with dimensionality reduction techniques.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
This document discusses modern perspectives on recommender systems and their applications at Mendeley. It covers the evolution of recommender problems from rating prediction to context-aware recommendations. It also discusses common recommender algorithms like collaborative filtering, content-based filtering and hybrid approaches. The document concludes by discussing how Mendeley uses recommenders for related research, researchers to follow, and other use cases.
This document discusses building an impersonal recommendation system using big data. It describes different recommendation approaches like collaborative filtering, knowledge-based, and content-based recommendations. An impersonal recommender provides suggestions without user profiles by analyzing customer purchase histories to find related item associations. The document proposes using Apache Hadoop to store and process large datasets for generating association rules to power recommendations. Elasticsearch would store and serve the rules to power an online recommender evaluation and improvement.
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
Social Recommender Systems Tutorial - WWW 2011idoguy
The document discusses social recommender systems and various approaches used in them. It covers fundamental recommendation techniques like collaborative filtering, content-based recommendation, and knowledge-based recommendation. It also discusses using tags, social relationships, and temporal data in recommendations. Evaluation of recommender systems and challenges are also summarized.
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.
Recommender systems are knowledge-based systems which support human decision-making. In an era of overwhelming choice, they help us decide which
products, services and information to consume. The focus of attention in recommender systems research and development has been on making recommendations to individual consumers. These places focus on the easier case, but ignore the fact that it is as common, if not more common, for us to consume items in groups such as couples, families and parties of friends. The choice of a date movie, a family holiday destination, or a restaurant for a celebration meal all require the balancing of the preferences of multiple consumers
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
Mendeley’s Research Catalogue: building it, opening it up and making it even ...Kris Jack
Presentation given at Workshop on Academic-Industrial Collaborations for Recommender Systems 2013 (http://bit.ly/114XDsE), JCDL'13. A walk through Mendeley as a platform, growing pains involved with engineering at a large scale, the data that we're making publicly available and some demos that have come out of academic collaborations.
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: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
This document provides an overview of recommendation engines and systems. It describes different types of recommendation approaches, including collaborative filtering, content-based filtering, and hybrid methods. It also discusses how recommendation algorithms work and are implemented in Apache Mahout, a machine learning library for developing scalable recommendation applications. Key recommendation techniques like item-based filtering and user-based filtering are explained.
The document discusses social recommender systems and how they can improve on traditional collaborative filtering approaches by incorporating trust relationships between users. It outlines research that used trust propagation algorithms to make recommendations for cold start users who lack sufficient rating histories. The author proposes to further explore how different types of social relationships (e.g. trust, friendship) differentially impact recommendation performance and to evaluate social and similarity-based collaborative filtering approaches.
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
Product Recommendations Enhanced with Reviewsmaranlar
Tutorial presented by Muthusamy Chelliah (Flipkart, India) and Sudeshna Sarkar (IIT Kharagpur, India) at ACM RecSys 2017 https://recsys.acm.org/recsys17/tutorials/#content-tab-1-3-tab
E-commerce websites commonly deploy recommender systems that make use of user activity (e.g., ratings, views, and purchases) or content (product descriptions). These recommender systems can benefit enormously by also exploiting the information contained in customer reviews. Reviews capture the experience of multiple customers with diverse preferences, often on the fine-grained level of specific features of products. Reviews can also identify consumers’ preferences for product features and provide helpful explanations. The usefulness of reviews is evidenced by the prevalence of their use by customers to support shopping decisions online. With the appropriate techniques, recommender systems can benefit directly from user reviews.
This tutorial will present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. Topics covered include text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions, and recommender algorithms that use review information to address the cold-start problem.
The tutorial sessions will be interspersed with examples from an online marketplace (i.e., Flipkart) and experience with using data mining and Natural Language Processing techniques (e.g., matrix factorization, LDA, word embeddings) from Web-scale systems.
Past present and future of Recommender Systems: an Industry PerspectiveXavier Amatriain
The document summarizes the past, present, and future of recommender systems from an industry perspective.
[1] In the past, Netflix popularized recommender systems with their 2006 Netflix Prize competition.
[2] Currently, recommender systems are used widely across many applications and industries. They have evolved to use implicit feedback and contextual information beyond just explicit ratings. Ranking items is also central to recommender systems.
[3] Future directions include addressing indirect feedback challenges, incorporating the value or reward of recommendations, optimizing full pages rather than just individual recommendations, and personalizing not just what is recommended but how it is recommended to users.
This document provides an overview of recommendation systems and collaborative filtering algorithms. It describes memory-based and model-based collaborative filtering, including user-based and item-based approaches. Challenges with recommendation systems like data sparsity and scalability are also discussed. The document demonstrates collaborative filtering using the Mahout library on Movielens data and outlines future work on improving scalability and developing real-time recommendations.
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
This document discusses data mining techniques and recommendation systems. It describes common data mining techniques like classification, clustering, regression, association rule mining and outlier analysis. It also discusses the knowledge discovery process and applications of data mining. The document then covers recommendation systems, describing content-based, collaborative filtering and hybrid recommendation approaches. It provides examples of these systems.
This document discusses recommender systems and approaches used at Netflix. It covers collaborative filtering using user-user and item-item methods, content-based recommendations using item attributes, and hybrid approaches. It provides examples of how Netflix uses collaborative filtering to generate personalized genre rows and social recommendations. Netflix combines many data sources and machine learning models to power its highly personalized recommendation engine.
This document provides an introduction to recommender systems. It discusses how recommender systems can help users filter through large amounts of information and options in an era of information overload. It describes different types of recommender systems, including content-based, collaborative filtering, and context-based recommender systems. The document also discusses challenges like sparsity in data and scaling to large datasets, and how modeling approaches can help address these challenges.
This document discusses building an impersonal recommendation system using big data. It describes different recommendation approaches like collaborative filtering, knowledge-based, and content-based recommendations. An impersonal recommender provides suggestions without user profiles by analyzing customer purchase histories to find related item associations. The document proposes using Apache Hadoop to store and process large datasets for generating association rules to power recommendations. Elasticsearch would store and serve the rules to power an online recommender evaluation and improvement.
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
Social Recommender Systems Tutorial - WWW 2011idoguy
The document discusses social recommender systems and various approaches used in them. It covers fundamental recommendation techniques like collaborative filtering, content-based recommendation, and knowledge-based recommendation. It also discusses using tags, social relationships, and temporal data in recommendations. Evaluation of recommender systems and challenges are also summarized.
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.
Recommender systems are knowledge-based systems which support human decision-making. In an era of overwhelming choice, they help us decide which
products, services and information to consume. The focus of attention in recommender systems research and development has been on making recommendations to individual consumers. These places focus on the easier case, but ignore the fact that it is as common, if not more common, for us to consume items in groups such as couples, families and parties of friends. The choice of a date movie, a family holiday destination, or a restaurant for a celebration meal all require the balancing of the preferences of multiple consumers
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
Mendeley’s Research Catalogue: building it, opening it up and making it even ...Kris Jack
Presentation given at Workshop on Academic-Industrial Collaborations for Recommender Systems 2013 (http://bit.ly/114XDsE), JCDL'13. A walk through Mendeley as a platform, growing pains involved with engineering at a large scale, the data that we're making publicly available and some demos that have come out of academic collaborations.
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: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
This document provides an overview of recommendation engines and systems. It describes different types of recommendation approaches, including collaborative filtering, content-based filtering, and hybrid methods. It also discusses how recommendation algorithms work and are implemented in Apache Mahout, a machine learning library for developing scalable recommendation applications. Key recommendation techniques like item-based filtering and user-based filtering are explained.
The document discusses social recommender systems and how they can improve on traditional collaborative filtering approaches by incorporating trust relationships between users. It outlines research that used trust propagation algorithms to make recommendations for cold start users who lack sufficient rating histories. The author proposes to further explore how different types of social relationships (e.g. trust, friendship) differentially impact recommendation performance and to evaluate social and similarity-based collaborative filtering approaches.
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
Product Recommendations Enhanced with Reviewsmaranlar
Tutorial presented by Muthusamy Chelliah (Flipkart, India) and Sudeshna Sarkar (IIT Kharagpur, India) at ACM RecSys 2017 https://recsys.acm.org/recsys17/tutorials/#content-tab-1-3-tab
E-commerce websites commonly deploy recommender systems that make use of user activity (e.g., ratings, views, and purchases) or content (product descriptions). These recommender systems can benefit enormously by also exploiting the information contained in customer reviews. Reviews capture the experience of multiple customers with diverse preferences, often on the fine-grained level of specific features of products. Reviews can also identify consumers’ preferences for product features and provide helpful explanations. The usefulness of reviews is evidenced by the prevalence of their use by customers to support shopping decisions online. With the appropriate techniques, recommender systems can benefit directly from user reviews.
This tutorial will present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. Topics covered include text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions, and recommender algorithms that use review information to address the cold-start problem.
The tutorial sessions will be interspersed with examples from an online marketplace (i.e., Flipkart) and experience with using data mining and Natural Language Processing techniques (e.g., matrix factorization, LDA, word embeddings) from Web-scale systems.
Past present and future of Recommender Systems: an Industry PerspectiveXavier Amatriain
The document summarizes the past, present, and future of recommender systems from an industry perspective.
[1] In the past, Netflix popularized recommender systems with their 2006 Netflix Prize competition.
[2] Currently, recommender systems are used widely across many applications and industries. They have evolved to use implicit feedback and contextual information beyond just explicit ratings. Ranking items is also central to recommender systems.
[3] Future directions include addressing indirect feedback challenges, incorporating the value or reward of recommendations, optimizing full pages rather than just individual recommendations, and personalizing not just what is recommended but how it is recommended to users.
This document provides an overview of recommendation systems and collaborative filtering algorithms. It describes memory-based and model-based collaborative filtering, including user-based and item-based approaches. Challenges with recommendation systems like data sparsity and scalability are also discussed. The document demonstrates collaborative filtering using the Mahout library on Movielens data and outlines future work on improving scalability and developing real-time recommendations.
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
This document discusses data mining techniques and recommendation systems. It describes common data mining techniques like classification, clustering, regression, association rule mining and outlier analysis. It also discusses the knowledge discovery process and applications of data mining. The document then covers recommendation systems, describing content-based, collaborative filtering and hybrid recommendation approaches. It provides examples of these systems.
This document discusses recommender systems and approaches used at Netflix. It covers collaborative filtering using user-user and item-item methods, content-based recommendations using item attributes, and hybrid approaches. It provides examples of how Netflix uses collaborative filtering to generate personalized genre rows and social recommendations. Netflix combines many data sources and machine learning models to power its highly personalized recommendation engine.
This document provides an introduction to recommender systems. It discusses how recommender systems can help users filter through large amounts of information and options in an era of information overload. It describes different types of recommender systems, including content-based, collaborative filtering, and context-based recommender systems. The document also discusses challenges like sparsity in data and scaling to large datasets, and how modeling approaches can help address these challenges.
Recommendations are everywhere : music, movies, books, social medias, e-commerce web sites… The Web is leaving the era of search and entering one of discovery. This quick introduction will help you to understand this vast topic and why you should use it.
Олександр Обєдніков “Рекомендательные системы”Dakiry
This document provides an overview of recommender systems. It begins with definitions and examples of recommender systems and their business value. It then discusses the problem formulation and history, including the Netflix Prize competition. Traditional collaborative filtering and latent factor models are explained. The document also covers content-based recommendations and novel approaches like learning to rank, sequence recommendation using deep learning, and social/trust-based systems. It concludes with a discussion of hybrid recommendation approaches.
recommendation system techunique and issueNutanBhor
This document discusses recommendation system techniques and issues. It covers common recommendation approaches like content-based filtering, collaborative filtering, and hybrid systems. It also addresses challenges like cold start problems, privacy issues, and data sparsity. Recommendation systems analyze user preferences to suggest new items, and are used by applications like ecommerce sites, streaming services, and social networks to provide personalized recommendations. While useful, they also present technical challenges for researchers.
The document provides an overview of recommender systems. It discusses the typical architecture of recommender systems and describes three main types: collaborative filtering systems, content-based systems, and knowledge-based systems. It also covers paradigms like collaborative filtering, content-based, knowledge-based, and hybrid recommender systems. The document then focuses on collaborative filtering techniques like user-based nearest neighbor collaborative filtering and item-based collaborative filtering. It also discusses latent factor models, matrix factorization approaches, and context-based recommender systems.
Lecture Notes on Recommender System IntroductionPerumalPitchandi
This document provides an overview of recommender systems and the techniques used to build them. It discusses collaborative filtering, content-based filtering, knowledge-based recommendations, and hybrid approaches. For collaborative filtering, it describes user-based and item-based approaches, including measuring similarity, making predictions, and generating recommendations. It also discusses evaluation techniques and advanced topics like explanations.
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
The Hive Think Tank: Machine Learning at Pinterest by Jure LeskovecThe Hive
Machine learning is at the core of Pinterest. Pinterest personalizes and ranks 1B+ pins, 700+ million boards for 100M+ users all over the world, using data gathered from collaborative filtering, user curation, web crawling, and more. At Pinterest we model relationships between pins, handle cold-start problems and deal with real-time recommendations.
In this presentation Jure gave an overview of the problems and effective solutions developed at Pinterest. He focused on systems and effective engineering choices made to enable productive machine learning development and enable multiple engineers effectively develop, test, and deploy machine-learned models.
Use of data science in recommendation systemAkashPatil334
This document discusses the use of data science in recommendation systems. It defines recommendation systems as systems that predict a user's preferences for items and recommend top items. It also defines data science as using scientific methods to extract knowledge from structured and unstructured data. The document then describes different types of recommendation systems, including collaborative filtering, content-based filtering, and hybrid systems. It provides examples of how Netflix, Amazon, LinkedIn, and Pandora use recommendation systems.
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
Recommender System _Module 1_Introduction to Recommender System.pptxSatyam Sharma
This document provides an introduction and overview of a module on recommender systems. The module aims to help students understand the importance and basic concepts of recommender systems. The syllabus covers introduction to recommender systems, different types of recommender systems including collaborative filtering, content-based, and knowledge-based systems. It also discusses hybrid systems, application and evaluation techniques, and emerging topics and challenges. The objective is for students to learn the basic concepts, understand different recommender system types, and be able to evaluate recommender systems as a multidisciplinary field.
Recommender systems allow online retailers to customize their sites to meet consumer tastes by aiding browsing and suggesting related items. Personalization is one of e-commerce's advantages over brick-and-mortar stores. Common techniques include item-to-item recommendations based on user ratings, user-to-user comparisons based on item preferences, and population-based suggestions of popular items. Challenges include obtaining user data, making novel recommendations, and addressing ethical issues.
This document discusses recommendation systems and how to develop them. It begins by introducing the speaker and an overview of the topics to be covered. It then explains what recommendation systems are and different types including search, content-based, and collaborative filtering. It discusses drawbacks like cold start problems and sparsity and ways to address them. The document concludes with tips for refining recommendation models like normalization, capturing trends, and temporal factors.
Julie Grundy gives an overview of user experience Design, why it's important, guiding principles, UX research overview, and tactics used by UX professionals. November 2015.
1. Fashion companies are leveraging data science and personalization to improve customers' shopping experience. Computer vision and deep learning allow them to use visual data from photos.
2. Recommendation systems combine traditional signals like ratings and clicks with visual signals from photos to improve accuracy.
3. Rigorous experimentation is important to test recommendation systems. Techniques like A/B testing and multi-armed bandits help optimize the personalized experience for each customer.
Modern Perspectives on Recommender Systems and their Applications in MendeleyMaya Hristakeva
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. Joint work with Kris Jack, Chief Data Scientist at Mendeley.
Demystifying recommender systems: know how recommendation systems workKnoldus Inc.
A lot of companies are leveraging the power of data to enhance customer experience and revenues with the help of personalized recommendations. These days, consumers expect personalized experiences from brands, and hence the use of recommendation systems is necessary and no longer just nice-to-have.
Data scientists play an important role here by helping companies deliver these experiences to their customers. The internet has given a huge number of options to people. For instance, there are millions of apps on the Google Play Store & billions of videos on YouTube. How can a user navigate through the maze of information and find what’s most relevant for him/her?
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~ How do they work?
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Sistema de recomendações de Filmes do Netflix
1. Xavier Amatriain – July 2014 – Recommender Systems
Recommender Systems
Collaborative Filtering and other approaches
Xavier Amatriain
Research/Engineering Director @ Netflix
MLSS ‘14
7. Xavier Amatriain – July 2014 – Recommender Systems
Recent/Upcoming Publications
● The Recommender Problem Revisited. KDD and Recsys 2014 Tutorial
● KDD: Big & Personal: data and models behind Netflix recommendations. 2013
● SIGKDD Explorations: Mining large streams of user data for personalized recommendations. 2012
● Recsys: Building industrial-scale real-world recommender systems. 2012
● Recys - Walk the Talk: Analyzing the relation between implicit and explicit feedback for preference elicitation. 2011
● SIGIR – Temporal behavior of CF. 2010
● Web Intelligence – Expert-based CF for music. 2010
● Recsys – Tensor Factorization. 2010
● Mobile HCI – Tourist Recommendation. 2010
● Recsys Handbook (book) – Data mining for recsys. 2010 & Recommender Systems in Industry. 2014
● SIGIR – Wisdom of the Few. 2009
● Recsys – Denoising by re-rating. 2009
● CARS – Implicit context-aware recommendations. 2009
● UMAP – I like it I like it not. 2009
8. Xavier Amatriain – July 2014 – Recommender Systems
Collaborators/Contributors
Justin Basilico
(Netflix)
Alexandros Karatzoglou
(Telefonica Research)
Francesco Ricci
(University of Bolzano)
Bamhshad Mobasher
(De Paul U)
Erik Bernhardsson
(Spotify)
9. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
10. Xavier Amatriain – July 2014 – Recommender Systems
1. Introduction: What is a
Recommender System?
11. Xavier Amatriain – July 2014 – Recommender Systems
The Age of Search has come to an end
• ... long live the Age of Recommendation!
• Chris Anderson in “The Long Tail”
• “We are leaving the age of information and entering the age
of recommendation”
• CNN Money, “The race to create a 'smart' Google”:
• “The Web, they say, is leaving the era of search and
entering one of discovery. What's the difference? Search is
what you do when you're looking for something. Discovery
is when something wonderful that you didn't know existed,
or didn't know how to ask for, finds you.”
12. Xavier Amatriain – July 2014 – Recommender Systems
Information overload
“People read around 10 MB worth of material a day, hear 400 MB a
day, and see 1 MB of information every second” - The Economist, November 2006
In 2015, consumption will raise to 74 GB a day - UCSD Study 2014
13. Xavier Amatriain – July 2014 – Recommender Systems
The value of recommendations
• Netflix: 2/3 of the movies watched are
recommended
• Google News: recommendations generate
38% more clickthrough
• Amazon: 35% sales from recommendations
• Choicestream: 28% of the people would buy
more music if they found what they liked.
u
14. Xavier Amatriain – July 2014 – Recommender Systems
The “Recommender problem”
● 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
○ …
15. Xavier Amatriain – July 2014 – Recommender Systems
The “Recommender problem”
• Let C be set of all users and let S be set of all possible
recommendable items
• Let u be a utility function measuring the usefulness of
item s to user c, i.e., u : C X S→R, where R is a totally
ordered set.
• For each user cєC, we want to choose items sєS that
maximize u.
• Utility is usually represented by rating but can be any function
17. Xavier Amatriain – July 2014 – Recommender Systems
Approaches to Recommendation
● Collaborative Filtering: Recommend items based only on the
users past behavior
○ User-based: Find similar users to me and recommend what
they liked
○ Item-based: Find similar items to those that I have
previously liked
● Content-based: Recommend based on item features
● Personalized Learning to Rank: Treat recommendation as a
ranking problem
● Demographic: Recommend based on user features
● Social recommendations (trust-based)
● Hybrid: Combine any of the above
18. Xavier Amatriain – July 2014 – Recommender Systems
Recommendation as data mining
The core of the Recommendation Engine can be
assimilated to a general data mining problem:
19. Xavier Amatriain – July 2014 – Recommender Systems
Machine Learning + all those other
things
● User Interface
● System requirements (efficiency, scalability,
privacy....)
● Serendipity
● ....
20. Xavier Amatriain – July 2014 – Recommender Systems
Serendipity
● Unsought finding
● Don't recommend items the user already knows
or would have found anyway.
● Expand the user's taste into neighboring areas
by improving the obvious
● Collaborative filtering can offer controllable
serendipity (e.g. controlling how many
neighbors to use in the recommendation)
21. Xavier Amatriain – July 2014 – Recommender Systems
What works
● Depends on the domain and particular problem
● However, in the general case it has been
demonstrated that the best isolated approach is CF.
○ Other approaches can be hybridized to improve
results in specific cases (cold-start problem...)
● What matters:
○ Data preprocessing: outlier removal, denoising,
removal of global effects (e.g. individual user's
average)
○ “Smart” dimensionality reduction using MF/SVD
○ Combining methods
22. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
23. Xavier Amatriain – July 2014 – Recommender Systems
2. Traditional Approaches
24. Xavier Amatriain – July 2014 – Recommender Systems
2.1. Collaborative Filtering
25. Xavier Amatriain – July 2014 – Recommender Systems
The CF Ingredients
● List of m Users and a list of n Items
● Each user has a list of items with associated opinion
○ Explicit opinion - a rating score
○ Sometime the rating is implicitly – purchase records
or listen to tracks
● Active user for whom the CF prediction task is
performed
● Metric for measuring similarity between users
● Method for selecting a subset of neighbors
● Method for predicting a rating for items not currently
rated by the active user.
26. Xavier Amatriain – July 2014 – Recommender Systems
Collaborative Filtering
The basic steps:
1. Identify set of ratings for the target/active user
2. Identify set of users most similar to the target/active user
according to a similarity function (neighborhood
formation)
3. Identify the products these similar users liked
4. Generate a prediction - rating that would be given by the
target user to the product - for each one of these products
5. Based on this predicted rating recommend a set of top N
products
27. Xavier Amatriain – July 2014 – Recommender Systems
Collaborative Filtering
● 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:
○ Requires a large number of reliable “user feedback data
points” to bootstrap
○ Requires products to be standardized (users should
have bought exactly the same product)
○ Assumes that prior behavior determines current
behavior without taking into account “contextual”
knowledge (session-level)
28. Xavier Amatriain – July 2014 – Recommender Systems
Personalised vs Non-Personalised CF
● CF recommendations are personalized since
the “prediction” is based on the ratings
expressed by similar users
○ Those neighbors are different for each
target user
● A non-personalized collaborative-based
recommendation can be generated by averaging
the recommendations of ALL the users
● How would the two approaches compare?
29. Xavier Amatriain – July 2014 – Recommender Systems
Personalised vs Non-Personalised CF
0,1510,2230,0222811718164974424EachMovie
0,1790,2330,041100020939526040MovieLens
0,1520,2200,725351944910048483Jester
MAE
Pers
MAE
Non
Pers
density
total
ratings
itemsusersData Set
Not much difference indeed!
vij
is the rating of user i for product
j and vj
is the average rating for
product j
30. Xavier Amatriain – July 2014 – Recommender Systems
Personalized vs. Not Personalized
● Netflix Prize's first
conclusion: it is
really extremely
simple to produce
“reasonable”
recommendations
and extremely
difficult to improve
them.
31. Xavier Amatriain – July 2014 – Recommender Systems
User-based Collaborative Filtering
32. Xavier Amatriain – July 2014 – Recommender Systems
User-User Collaborative
Filtering
Target User
Weighted
Sum
33. Xavier Amatriain – July 2014 – Recommender Systems
UB Collaborative Filtering
● 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
as
• Similarity can be computed by Pearson correlation
or
or
34. Xavier Amatriain – July 2014 – Recommender Systems
User-based CF
Example
35. Xavier Amatriain – July 2014 – Recommender Systems
User-based CF
Example
36. Xavier Amatriain – July 2014 – Recommender Systems
User-based CF
Example
37. Xavier Amatriain – July 2014 – Recommender Systems
User-based CF
Example
38. Xavier Amatriain – July 2014 – Recommender Systems
User-based CF
Example
39. Xavier Amatriain – July 2014 – Recommender Systems
Challenges Of User-based CF
Algorithms
● Sparsity – evaluation of large item sets, users purchases
are under 1%.
● Difficult to make predictions based on nearest neighbor
algorithms =>Accuracy of recommendation may be poor.
● Scalability - Nearest neighbor require computation that
grows with both the number of users and the number of
items.
● Poor relationship among like minded but sparse-rating
users.
● Solution : usage of latent models to capture similarity
between users & items in a reduced dimensional space.
40. Xavier Amatriain – July 2014 – Recommender Systems
Item-based Collaborative Filtering
41. Xavier Amatriain – July 2014 – Recommender Systems
Item-Item Collaborative
Filtering
42. Xavier Amatriain – July 2014 – Recommender Systems
Item Based CF Algorithm
● Look into the items the target user has rated
● Compute how similar they are to the target item
○ Similarity only using past ratings from other
users!
● Select k most similar items.
● Compute Prediction by taking weighted average
on the target user’s ratings on the most similar
items.
43. Xavier Amatriain – July 2014 – Recommender Systems
Item Similarity Computation
● Similarity between items i & j computed by finding
users who have rated them and then applying a
similarity function to their ratings.
● Cosine-based Similarity – items are vectors in the m
dimensional user space (difference in rating scale
between users is not taken into account).
44. Xavier Amatriain – July 2014 – Recommender Systems
Item Similarity Computation
● Correlation-based Similarity - using the Pearson-r
correlation (used only in cases where the users rated
both item I & item j).
• Ru,i
= rating of user u on item i.
• Ri
= average rating of the i-th item.
45. Xavier Amatriain – July 2014 – Recommender Systems
Item Similarity Computation
● Adjusted Cosine Similarity – each pair in the co-rated
set corresponds to a different user. (takes care of
difference in rating scale).
• Ru,i
= rating of user u on item i.
• Ru
= average of the u-th user.
46. Xavier Amatriain – July 2014 – Recommender Systems
Prediction Computation
● Generating the prediction – look into the target
users ratings and use techniques to obtain
predictions.
● Weighted Sum – how the active user rates the
similar items.
47. Xavier Amatriain – July 2014 – Recommender Systems
Item-based CF Example
48. Xavier Amatriain – July 2014 – Recommender Systems
Item-based CF Example
49. Xavier Amatriain – July 2014 – Recommender Systems
Item-based CF Example
50. Xavier Amatriain – July 2014 – Recommender Systems
Item-based CF Example
51. Xavier Amatriain – July 2014 – Recommender Systems
Item-based CF Example
52. Xavier Amatriain – July 2014 – Recommender Systems
Item-based CF Example
53. Xavier Amatriain – July 2014 – Recommender Systems
Performance Implications
● Bottleneck - Similarity computation.
● Time complexity, highly time consuming with
millions of users and items in the database.
○ Isolate the neighborhood generation and
predication steps.
○ “off-line component” / “model” – similarity
computation, done earlier & stored in memory.
○ “on-line component” – prediction generation
process.
54. Xavier Amatriain – July 2014 – Recommender Systems
Recap: challenges of Nearest-
neighbor Collaborative Filtering
55. Xavier Amatriain – July 2014 – Recommender Systems
The Sparsity Problem
● Typically: large product sets, user ratings for a small
percentage of them
● Example Amazon: millions of books and a user may
have bought hundreds of books –
○ the probability that two users that have bought 100 books
have a common book (in a catalogue of 1 million books) is
0.01 (with 50 and 10 millions is 0.0002).
● Standard CF must have a number of users
comparable to one tenth of the size of the product
catalogue
56. Xavier Amatriain – July 2014 – Recommender Systems
The Sparsity Problem
● If you represent the Netflix Prize rating data in a
User/Movie matrix you get...
○ 500,000 x 17,000 = 8,500 M positions
○ Out of which only 100M are not 0's!
● Methods of dimensionality reduction
○ Matrix Factorization
○ Clustering
○ Projection (PCA ...)
57. Xavier Amatriain – July 2014 – Recommender Systems
The Scalability Problem
● Nearest neighbor algorithms require computations that
grows with both the number of customers and
products
● With millions of customers and products a web-based
recommender can suffer serious scalability problems
● The worst case complexity is O(mn) (m customers and
n products)
● But in practice the complexity is O(m + n) since for
each customer only a small number of products are
considered
● Some clustering techniques like K-means can help
58. Xavier Amatriain – July 2014 – Recommender Systems
Performance Implications
● User-based CF – similarity between users is
dynamic, precomupting user neighborhood can
lead to poor predictions.
● Item-based CF – similarity between items is
static.
● enables precomputing of item-item similarity =>
prediction process involves only a table lookup for
the similarity values & computation of the
weighted sum.
59. Xavier Amatriain – July 2014 – Recommender Systems
Other approaches to CF
60. Xavier Amatriain – July 2014 – Recommender Systems
Model-based
Collaborative Filtering
61. Xavier Amatriain – July 2014 – Recommender Systems
Model Based CF Algorithms
● Memory based
○ Use the entire user-item database to generate a
prediction.
○ Usage of statistical techniques to find the neighbors – e.g.
nearest-neighbor.
● Memory based
○ 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
■ ...
62. Xavier Amatriain – July 2014 – Recommender Systems
Model-based CF:
What we learned from the
Netflix Prize
63. Xavier Amatriain – July 2014 – Recommender Systems
What we were interested in:
■ High quality recommendations
Proxy question:
■ Accuracy in predicted rating
■ Improve by 10% = $1million!
64. Xavier Amatriain – July 2014 – Recommender Systems
2007 Progress Prize
▪ Top 2 algorithms
▪ SVD - Prize RMSE: 0.8914
▪ RBM - Prize RMSE: 0.8990
▪ Linear blend Prize RMSE: 0.88
▪ Currently in use as part of Netflix’ rating prediction
component
▪ Limitations
▪ Designed for 100M ratings, we have 5B ratings
▪ Not adaptable as users add ratings
▪ Performance issues
65. Xavier Amatriain – July 2014 – Recommender Systems
SVD/MF
X[n x m] = U[n x r] S [ r x r] (V[m x r])T
● X: m x n matrix (e.g., m users, n videos)
● U: m x r matrix (m users, r factors)
● S: r x r diagonal matrix (strength of each ‘factor’) (r: rank of the
matrix)
● V: r x n matrix (n videos, r factor)
66. Xavier Amatriain – July 2014 – Recommender Systems
Simon Funk’s SVD
● One of the most
interesting findings
during the Netflix
Prize came out of
a blog post
● Incremental,
iterative, and
approximate way
to compute the
SVD using
gradient descent
67. Xavier Amatriain – July 2014 – Recommender Systems
▪ User factor vectors and item-factors vector
▪ Baseline (bias) (user & item deviation from average)
▪ Predict rating as
▪ SVD++ (Koren et. Al) asymmetric variation w. implicit feedback
▪ Where
▪ are three item factor vectors
▪ Users are not parametrized, but rather represented by:
▪ R(u): items rated by user u
▪ N(u): items for which the user has given implicit preference (e.g. rated vs. not rated)
SVD for Rating Prediction
68. Xavier Amatriain – July 2014 – Recommender Systems
Artificial Neural Networks – 4 generations
● 1st - Perceptrons (~60s)
○ Single layer of hand-coded features
○ Linear activation function
○ Fundamentally limited in what they can learn to do.
● 2nd - Back-propagation (~80s)
○ Back-propagate error signal to get derivatives for
learning
○ Non-linear activation function
● 3rd - Belief Networks (~90s)
○ Directed acyclic graph composed of (visible &
hidden) stochastic variables with weighted
connections.
○ Infer the states of the unobserved variables &
learn interactions between variables to make
network more likely to generate observed data.
69. Xavier Amatriain – July 2014 – Recommender Systems
Restricted Boltzmann Machines
● Each unit is a state that can be active or not active
● Each input to a unit is associated to a weight
● The transfer function calculates a score for every unit
based on the weighted sum of inputs
● Score is passed to the activation function that calculates
the probability of the unit to be active
70. Xavier Amatriain – July 2014 – Recommender Systems
Restricted Boltzmann Machines
● Restrict the connectivity to make learning
easier.
○ Only one layer of hidden units.
○ Although multiple layers are possible
○ No connections between hidden units.
○ Hidden units are independent given the visible
states..
○ So we can quickly get an unbiased sample from the
posterior distribution over hidden “causes” when
given a data-vector
● RBMs can be stacked to form Deep Belief
Networks (DBN) – 4th generation of ANNs
71. Xavier Amatriain – July 2014 – Recommender Systems
RBM for the Netflix Prize
72. Xavier Amatriain – July 2014 – Recommender Systems
RBM for Recommendations
73. Xavier Amatriain – July 2014 – Recommender Systems
RBM for Recommendations
● Each unit in the visible layer is an item
● The number of hidden units is a parameter
● In the training phase, for each user:
○ If the user rated the item, vi is activated
○ Activation states of all vi are the inputs to hj
○ Based on the activation, hj is computed
○ The activation state of hj becomes input to vi
○ The activation state of vi is recalculated
○ Difference between current and past activation state
for vi is used to update the weights wij and the
thresholds
74. Xavier Amatriain – July 2014 – Recommender Systems
RBM for Recommendations
● In the prediction phase with a trained RBM:
○ For the items of the user the vi are activated
○ Based on this the state of the hj is computed
○ The activation of hj is used as input to recompute the
state of vi
○ Activation probabilities are used to recommend items
75. Xavier Amatriain – July 2014 – Recommender Systems
Putting all together
● Remember that current production
model includes an ensemble of both
SVD++ and RBMs
77. Xavier Amatriain – July 2014 – Recommender Systems
Clustering
● Another way to make recommendations
based on past purchases is to cluster
customers
● Each cluster will be assigned typical
preferences, based on preferences of
customers who belong to the cluster
● Customers within each cluster will receive
recommendations computed at the cluster
level
78. Xavier Amatriain – July 2014 – Recommender Systems
Clustering
Customers B, C and D are « clustered » together.
Customers A and E are clustered into another separate
group
• « Typical » preferences for CLUSTER are:
• Book 2, very high
• Book 3, high
• Books 5 and 6, may be recommended
• Books 1 and 4, not recommended at all
79. Xavier Amatriain – July 2014 – Recommender Systems
Clustering
How does it work?
• Any customer that shall be classified as a member of
CLUSTER will receive recommendations based on
preferences of the group:
• Book 2 will be highly recommended to Customer F
• Book 6 will also be recommended to some extent
80. Xavier Amatriain – July 2014 – Recommender Systems
Clustering
Pros:
● Clustering techniques can be used to work on aggregated
data
● Can also be applied as a first step for shrinking the selection
of relevant neighbors in a collaborative filtering algorithm and
improve performance
● Can be used to capture latent similarities between users or
items
Cons:
● Recommendations (per cluster) may be less relevant than
collaborative filtering (per individual)
81. Xavier Amatriain – July 2014 – Recommender Systems
Locality-sensitive Hashing (LSH)
● Method for grouping similar items in highly
dimensional spaces
● Find a hashing function s.t. similar items are
grouped in the same buckets
● Main application is Nearest-neighbors
○ Hashing function is found iteratively by
concatenating random hashing functions
○ Addresses one of NN main concerns:
performance
83. Xavier Amatriain – July 2014 – Recommender Systems
Association rules
• Past purchases are transformed into
relationships of common purchases
84. Xavier Amatriain – July 2014 – Recommender Systems
Association rules
● These association rules are then used to made
recommendations
● If a visitor has some interest in Book 5, she will be
recommended to buy Book 3 as well
● Recommendations are constrained to some minimum
levels of confidence
85. Xavier Amatriain – July 2014 – Recommender Systems
Association rules
Pros:
● Fast to implement (A priori algorithm for frequent itemset
mining)
● Fast to execute
● Not much storage space required
● Not « individual » specific
● Very successful in broad applications for large
populations, such as shelf layout in retail stores
Cons:
● Not suitable if knowledge of preferences change rapidly
● It is tempting to not apply restrictive confidence rules
→ May lead to literally stupid recommendations
87. Xavier Amatriain – July 2014 – Recommender Systems
Classifiers
● Classifiers are general computational models trained
using positive and negative examples
● They may take in inputs:
○ Vector of item features (action / adventure, Bruce
Willis)
○ Preferences of customers (like action / adventure)
○ Relations among item
● E.g. Logistic Regression, Bayesian Networks,
Support Vector Machines, Decision Trees, etc...
88. Xavier Amatriain – July 2014 – Recommender Systems
Classifiers
● Classifiers can be used in CF and CB
Recommenders
● Pros:
○ Versatile
○ Can be combined with other methods to improve
accuracy of recommendations
● Cons:
○ Need a relevant training set
○ May overfit (Regularization)
89. Xavier Amatriain – July 2014 – Recommender Systems
Limitations of
Collaborative Filtering
90. Xavier Amatriain – July 2014 – Recommender Systems
Limitations of Collaborative Filtering
● Cold Start: There needs to be enough other users
already in the system to find a match. New items
need to get enough ratings.
● Popularity Bias: Hard to recommend items to
someone with unique tastes.
○ Tends to recommend popular items (items from
the tail do not get so much data)
91. Xavier Amatriain – July 2014 – Recommender Systems
Cold-start
● New User Problem: To make accurate
recommendations, the system must first learn the
user’s preferences from the ratings.
○ Several techniques proposed to address this. Most
use the hybrid recommendation approach, which
combines content-based and collaborative
techniques.
● New Item Problem: New items are added regularly to
recommender systems. Until the new item is rated by
a substantial number of users, the recommender
system is not able to recommend it.
92. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
93. Xavier Amatriain – July 2014 – Recommender Systems
2.2 Content-based Recommenders
94. Xavier Amatriain – July 2014 – Recommender Systems
Content-Based Recommendations
● Recommendations based on information on the content of
items rather than on other users’ opinions/interactions
● Use a machine learning algorithm to induce a model of the
users preferences from examples based on a featural
description of content.
● In content-based recommendations, the system tries to
recommend items similar to those a given user has liked in
the past
● A pure content-based recommender system makes
recommendations for a user based solely on the profile built
up by analyzing the content of items which that user has
rated in the past.
95. Xavier Amatriain – July 2014 – Recommender Systems
What is content?
● What is the content of an item?
● It can be explicit attributes or characteristics of the
item. For example for a film:
○ Genre: Action / adventure
○ Feature: Bruce Willis
○ Year: 1995
● It can also be textual content (title, description, table
of content, etc.)
○ Several techniques to compute the distance between two
textual documents
○ Can use NLP techniques to extract content features
● Can be extracted from the signal itself (audio, image)
96. Xavier Amatriain – July 2014 – Recommender Systems
Content-Based Recommendation
● Common for recommending text-based products (web
pages, usenet news messages, )
● 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)
○ Text documents recommended based on a comparison between their
content (words appearing) and user model (a set of preferred words)
● The user model can also be a classifier based on
whatever technique (Neural Networks, Naïve Bayes...)
97. Xavier Amatriain – July 2014 – Recommender Systems
Advantages of CB Approach
● No need for data on other users.
○ No cold-start or sparsity problems.
● Able to recommend to users with unique tastes.
● Able to recommend new and unpopular items
○ No first-rater problem.
● Can provide explanations of recommended items by
listing content-features that caused an item to be
recommended.
98. Xavier Amatriain – July 2014 – Recommender Systems
Disadvantages of CB Approach
● Requires content that can be encoded as meaningful
features.
● Some kind of items are not amenable to easy feature
extraction methods (e.g. movies, music)
● Even for texts, IR techniques cannot consider multimedia
information, aesthetic qualities, download time…
○ If you rate positively a page it could be not related to the presence of certain
keywords
● Users’ tastes must be represented as a learnable function of
these content features.
● Hard to exploit quality judgements of other users.
● Difficult to implement serendipity
● Easy to overfit (e.g. for a user with few data points we may
“pigeon hole” her)
99. Xavier Amatriain – July 2014 – Recommender Systems
Content-based Methods
• Let Content(s) be an item profile, i.e. a set of
attributes characterizing item s.
• Content usually described with keywords.
• “Importance” (or “informativeness”) of word kj
in
document dj
is determined with some weighting
measure wij
• One of the best-known measures in IR is the term
frequency/inverse document frequency (TF-IDF).
100. Xavier Amatriain – July 2014 – Recommender Systems
Content-based User Profile
● Let ContentBasedProfile(c) be the profile of user c
containing preferences of this user profiles are
obtained by:
○ analyzing the content of the previous items
○ using keyword analysis techniques
● For example, ContentBasedProfile(c) can be defined
as a vector of weights (wc1
, . . . , wck
), where weight
wci
denotes the importance of keyword ki to user c
101. Xavier Amatriain – July 2014 – Recommender Systems
Similarity Measures
• In content-based systems, the utility function u(c,s) is
usually defined as:
• Both ContentBasedProfile(c) of user c and Content(s)
of document s can be represented as TF-IDF vectors
of keyword weights.
102. Xavier Amatriain – July 2014 – Recommender Systems
Similarity Measurements
• Utility function u(c,s) usually represented by some
scoring heuristic defined in terms of vectors , such as
the cosine similarity measure.
103. Xavier Amatriain – July 2014 – Recommender Systems
Statistical and Machine Learning
Approaches
Other techniques are feasible
● Bayesian classifiers and various machine learning techniques,
including clustering, decision trees, and artificial neural
networks.
These methods use models learned from the underlying
data rather than heuristics.
● For example, based on a set of Web pages that were rated as
“relevant” or “irrelevant” by the user, the naive bayesian
classifier can be used to classify unrated Web pages.
104. Xavier Amatriain – July 2014 – Recommender Systems
Content-based Recommendation. An
unrealistic example
● An (unrealistic) example: how to compute
recommendations between 8 books based only on
their title?
• A customer is interested in the following book:”Building
data mining applications for CRM”
• Books selected:
• Building data mining applications for CRM
• Accelerating Customer Relationships: Using CRM and Relationship Technologies
• Mastering Data Mining: The Art and Science of Customer Relationship Management
• Data Mining Your Website
• Introduction to marketing
• Consumer behavior
• marketing research, a handbook
• Customer knowledge management
107. Xavier Amatriain – July 2014 – Recommender Systems
Content-based Recommendation
•The system computes distances between this book and
the 7 others
•The « closest » books are recommended:
• #1: Data Mining Your Website
• #2: Accelerating Customer Relationships: Using CRM and
Relationship Technologies
• #3: Mastering Data Mining: The Art and Science of Customer
Relationship Management
• Not recommended: Introduction to marketing
• Not recommended: Consumer behavior
• Not recommended: marketing research, a handbook
• Not recommended: Customer knowledge management
112. Xavier Amatriain – July 2014 – Recommender Systems
Ranking
● Most recommendations are presented in a sorted
list
● Recommendation can be understood as a ranking
problem
● Popularity is the obvious baseline
● Ratings prediction is a clear secondary data input
that allows for personalization
● Many other features can be added
118. Xavier Amatriain – July 2014 – Recommender Systems
Learning to rank
● Machine learning problem: goal is to construct
ranking model from training data
● Training data can be a partial order or binary
judgments (relevant/not relevant).
● Resulting order of the items typically induced
from a numerical score
● Learning to rank is a key element for
personalization
● You can treat the problem as a standard
supervised classification problem
119. Xavier Amatriain – July 2014 – Recommender Systems
Learning to rank - Metrics
● Quality of ranking measured using metrics as
○ Normalized Discounted Cumulative Gain
○ Mean Reciprocal Rank (MRR)
○ Fraction of Concordant Pairs (FCP)
○ Others…
● But, it is hard to optimize machine-learned models
directly on these measures (they are not
differentiable)
● Recent research on models that directly optimize
ranking measures
120. Xavier Amatriain – July 2014 – Recommender Systems
Learning to rank - Approaches
1. Pointwise
■ Ranking function minimizes loss function defined on
individual relevance judgment
■ Ranking score based on regression or classification
■ Ordinal regression, Logistic regression, SVM, GBDT, …
2. Pairwise
■ Loss function is defined on pair-wise preferences
■ Goal: minimize number of inversions in ranking
■ Ranking problem is then transformed into the binary
classification problem
■ RankSVM, RankBoost, RankNet, FRank…
121. Xavier Amatriain – July 2014 – Recommender Systems
Learning to rank - Approaches
3. Listwise
■ Indirect Loss Function
− RankCosine: similarity between ranking list
and ground truth as loss function
− ListNet: KL-divergence as loss function by
defining a probability distribution
− Problem: optimization of listwise loss function
may not optimize IR metrics
■ Directly optimizing IR measures (difficult since they
are not differentiable)
122. Xavier Amatriain – July 2014 – Recommender Systems
Learning to rank - Approaches
3. Listwise
■ Directly optimize IR measures through Genetic
Programming
■ Directly optimize measures with Simulated Annealing
■ Gradient descent on smoothed version of objective
function (e.g. CLiMF presented at Recsys 2012 or
TFMAP at SIGIR 2012)
■ SVM-MAP relaxes the MAP metric by adding it to the
SVM constraints
■ AdaRank uses boosting to optimize NDCG
123. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
124. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware
Recommendation
125. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
126. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
127. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
128. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
129. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
130. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
131. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
132. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
133. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
134. Xavier Amatriain – July 2014 – Recommender Systems
Context-aware Recommendations
135. Xavier Amatriain – July 2014 – Recommender Systems
Two dimensional model
165. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
166. Xavier Amatriain – July 2014 – Recommender Systems
3.3 Deep Learning for
Recommendation
167. Xavier Amatriain – July 2014 – Recommender Systems
Deep Learning for Collaborative
Filtering
● Let’s look at how Spotify uses Recurrent Networks
for Playlist Prediction (http://erikbern.com/?p=589)
168. Xavier Amatriain – July 2014 – Recommender Systems
Deep Learning for Collaborative
Filtering
● We assume is a normal distribution, log-likelihood of the
loss is just the (negative) L2 loss:
● We can specify that and that
○ Model is now completely specified and we have unknown
parameters
○ Find U, V, and W to maximize log likelihood over all
examples using backpropagation
169. Xavier Amatriain – July 2014 – Recommender Systems
Deep Learning for Collaborative
Filtering
● In order to predict the next track or movie a user is going to
watch, we need to define a distribution
○ If we choose Softmax as it is common practice, we get:
● Problem: denominator (over all examples is very
expensive to compute)
● Solution: build a tree that implements a hierarchical
softmax
● More details on the blogpost
170. Xavier Amatriain – July 2014 – Recommender Systems
ANN Training over GPUS and AWS
● How did we implement our ANN solution at Netflix?
http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html
171. Xavier Amatriain – July 2014 – Recommender Systems
ANN Training over GPUS and AWS
Level 1 distribution: machines over different AWS regions
Level 2 distribution: machines in AWS and same AWS region
Use coordination tools
Spearmint or similar for parameter optimization
Condor, StarCluster, Mesos… for distributed cluster
coordination
Level 3 parallelization: highly optimized parallel CUDA code
on GPUs
172. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
173. Xavier Amatriain – July 2014 – Recommender Systems
3.4 Similarity as Recommendation
174. Xavier Amatriain – July 2014 – Recommender Systems
What is similarity?
Similarity can refer to different dimensions
Similar in metadata/tags
Similar in user play behavior
Similar in user rating behavior
…
You can learn a model for each of them and finally learn an
ensemble
175. Xavier Amatriain – July 2014 – Recommender Systems
Graph-based similarities
0.8
0.2
0.3
0.
4
0.3
0.7
0.
3
176. Xavier Amatriain – July 2014 – Recommender Systems
Example of graph-based similarity: SimRank
▪ SimRank (Jeh & Widom, 02): “two objects are
similar if they are referenced by similar
objects.”
177. Xavier Amatriain – July 2014 – Recommender Systems
Similarity ensembles
Come up with a score of play similarity, rating similarity, tag-
based similarity…
Combine them using an ensemble
Weights are learned using regression over existing
response
Or… some MAB explore/exploit approach
The final concept of “similarity” responds to what users vote
as similar
178. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
179. Xavier Amatriain – July 2014 – Recommender Systems
3.5 Social Recommendations
180. Xavier Amatriain – July 2014 – Recommender Systems
Social and Trust-based
recommenders
● A social recommender system recommends items that
are “popular” in the social proximity of the user.
● A person being close in our social network does not
mean we trust their judgement
● This idea of trust is central in social-based systems
● It can be a general per-user value that takes into
account social proximity but can also be topic-specific
181. Xavier Amatriain – July 2014 – Recommender Systems
Defining Trust
● Trust is very complex
○ Involves personal background, history of interaction,
context, similarity, reputation, etc.
● Sociological definitions
○ Trust requires a belief and a commitment
○ E.g. Bob believes Frank will provide reliable information
thus Bob is willing to act on that information
○ Similar to a bet
● In the context of recommender systems, trust is
generally used to describe similarity in opinion
○ Ignores authority, correctness on facts
182. Xavier Amatriain – July 2014 – Recommender Systems
Trust Inference
The Goal: Select two individuals - the source (node
A) and sink (node C) - and recommend to the
source how much to trust the sink.
183. Xavier Amatriain – July 2014 – Recommender Systems
Major Algorithms - Networks
● Advogato (Levien)
● Appleseed (Ziegler and Lausen)
● MoleTrust (Massa and Avesani)
● TidalTrust (Golbeck)
184. Xavier Amatriain – July 2014 – Recommender Systems
Building Recommender Systems
Using Trust
● Use trust as a way to give more weight to
some users
● Trust for collaborative filtering
○ Use trust in place of (or combined with) similarity
● Trust for sorting and filtering
○ Prioritize information from trusted sources
185. Xavier Amatriain – July 2014 – Recommender Systems
Other ways to use Social
● Social connections can be used in
combination with other approaches
● In particular, “friendships” can be fed into
collaborative filtering methods in different
ways
− e.g. replace or modify user-user “similarity” by
using social network information
186. Xavier Amatriain – July 2014 – Recommender Systems
Demographic Methods
● Aim to categorize the user based on personal
attributes and make recommendation based
on demographic classes
● Demographic groups can come from
marketing research – hence experts decided
how to model the users
● Demographic techniques form people-to-
people correlations
187. Xavier Amatriain – July 2014 – Recommender Systems
Demographic Methods
● Demographic features in general are asked
● But can also induced by classifying a user using other
user descriptions (e.g. the home page) – you need
some user for which you know the class (e.g.
male/female)
● Prediction can use whatever learning mechanism we
like (nearest neighbor, naïve classifier, etc.)
188. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
190. Xavier Amatriain – July 2014 – Recommender Systems
Comparison of methods (FAB
system)
• Content–based
recommendation with
Bayesian classifier
• Collaborative is
standard using
Pearson correlation
• Collaboration via
content uses the
content-based user
profiles
Averaged on 44 users
Precision computed in top 3 recommendations
191. Xavier Amatriain – July 2014 – Recommender Systems
Hybridization Methods
Hybridization Method Description
Weighted Outputs from several techniques (in the form of
scores or votes) are combined with different
degrees of importance to offer final
recommendations
Switching Depending on situation, the system changes from
one technique to another
Mixed Recommendations from several techniques are
presented at the same time
Feature combination Features from different recommendation sources
are combined as input to a single technique
Cascade The output from one technique is used as input of
another that refines the result
Feature augmentation The output from one technique is used as input
features to another
Meta-level The model learned by one recommender is used
as input to another
192. Xavier Amatriain – July 2014 – Recommender Systems
Weighted
● Combine the results of different recommendation
techniques into a single recommendation list
○ Example 1: a linear combination of recommendation scores
○ Example 2: treats the output of each recommender
(collaborative, content-based and demographic) as a set of
votes, which are then combined in a consensus scheme
● Assumption: relative value of the different techniques is
more or less uniform across the space of possible items
○ Not true in general: e.g. a collaborative recommender will be
weaker for those items with a small number of raters.
193. Xavier Amatriain – July 2014 – Recommender Systems
Switching
● The system uses criterion to switch between techniques
○ Example: The DailyLearner system uses a content-
collaborative hybrid in which a content-based recommendation
method is employed first
○ If the content-based system cannot make a
recommendation with sufficient confidence, then a
collaborative recommendation is attempted
○ Note that switching does not completely avoid the cold-
start problem, since both the collaborative and the content-
based systems have the “new user” problem
● The main problem of this technique is to identify a GOOD
switching condition.
194. Xavier Amatriain – July 2014 – Recommender Systems
Mixed
● Recommendations from more than one technique are
presented together
● The mixed hybrid avoids the “new item” start-up problem
● It does not get around the “new user” start-up problem,
since both the content and collaborative methods need
some data about user preferences to start up.
195. Xavier Amatriain – July 2014 – Recommender Systems
Feature Combination
● Features can be combined in several directions. E.g.
○ (1) Treat collaborative information (ratings of users) as
additional feature data associated with each example
and use content-based techniques over this
augmented data set
○ (2) Treat content features as different dimensions for
the collaborative setting (i.e. as other ratings from
virtual specialized users)
196. Xavier Amatriain – July 2014 – Recommender Systems
Cascade
● One recommendation technique is employed first to
produce a coarse ranking of candidates and a second
technique refines the recommendation
○ Example: EntreeC uses its knowledge of restaurants to make
recommendations based on the user’s stated interests. The
recommendations are placed in buckets of equal preference,
and the collaborative technique is employed to break ties
● Cascading allows the system to avoid employing the
second, lower-priority, technique on items that are
already well-differentiated by the first
● But requires a meaningful and constant ordering of the
techniques.
197. Xavier Amatriain – July 2014 – Recommender Systems
Feature Augmentation
● Produce a rating or classification of an item and that
information is then incorporated into the processing of
the next recommendation technique
○ Example: Libra system makes content-based
recommendations of books based on data found in Amazon.
com, using a naive Bayes text classifier
○ In the text data used by the system is included “related
authors” and “related titles” information that Amazon generates
using its internal collaborative systems
● Very similar to the feature combination method:
○ Here the output is used for a second RS
○ In feature combination the representations used by two
systems are combined.
198. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
199. Xavier Amatriain – July 2014 – Recommender Systems
5. Netflix as a practical example
200. Xavier Amatriain – July 2014 – Recommender Systems
What we were interested in:
▪ High quality recommendations
Proxy question:
▪ Accuracy in predicted rating
▪ Improve by 10% = $1million! • Top 2 algorithms still in
production
Results
SVD
RBM
201. Xavier Amatriain – July 2014 – Recommender Systems
What about the final prize
ensembles?
● Our offline studies showed they were too
computationally intensive to scale
● Expected improvement not worth the engineering
effort
● Plus…. Focus had already shifted to other issues
that had more impact than rating prediction.
202. Xavier Amatriain – July 2014 – Recommender Systems
From the Netflix Prize
to today
2006 2014
203. Xavier Amatriain – July 2014 – Recommender Systems
Anatomy of
Netflix
Personalization
Everything is a Recommendation
212. Xavier Amatriain – July 2014 – Recommender Systems
Genre rows
● Personalized genre rows focus on user interest
○ Also provide context and “evidence”
○ Important for member satisfaction – moving personalized
rows to top on devices increased retention
● How are they generated?
○ Implicit: based on user’s recent plays, ratings, & other
interactions
○ Explicit taste preferences
○ Hybrid:combine the above
● Also take into account:
○ Freshness - has this been shown before?
○ Diversity– avoid repeating tags and genres, limit number of
TV genres, etc.
213. Xavier Amatriain – July 2014 – Recommender Systems
Genres - personalization
214. Xavier Amatriain – July 2014 – Recommender Systems
Genres - personalization
215. Xavier Amatriain – July 2014 – Recommender Systems
▪ Displayed in many
different contexts
▪ In response to user
actions/context
(search, queue
add…)
▪ More like… rows
Similars
231. Metadata
● Tag space is made of
thousands of different
concepts
● Items are manually annotated
● Metadata is useful
○ Especially for coldstart
232. Social
● Can your “friends” interests help us better predict
yours?
● The answer is similar to the Metadata case:
○ If we know enough about you, social information
becomes less useful
○ But, it is very interesting for coldstarting
● And… social support for recommendations has
been shown to matter
233. Impressions
● We track what users get shown and their
interactions
● Impression tracking is costly from an infrastructure
perspective
● But, it can have very important applications
○ Correcting for presentation bias
○ Different forms of positive and negative feedback
○ ...
234. Smart Models ■ Regression models (Logistic,
Linear, Elastic nets)
■ GBDT/RF
■ SVD & other MF models
■ Factorization Machines
■ Restricted Boltzmann
Machines
■ Markov Chains & other
graphical models
■ Clustering (from k-means to
HDP)
■ Deep ANN
■ LDA
■ Association Rules
■ …
235. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
237. Xavier Amatriain – July 2014 – Recommender Systems
Conclusions
● For many applications such as Recommender
Systems (but also Search, Advertising, and even
Networks) understanding data and users is vital
● Algorithms can only be as good as the data they
use as input
○ But the inverse is also true: you need a good
algorithm to leverage your data
● Importance of User/Data Mining is going to be a
growing trend in many areas in the coming years
238. Xavier Amatriain – July 2014 – Recommender Systems
Conclusions
● Recommender Systems (RS) is an important
application of User Mining
● RS have the potential to become as important
as Search is now
● However, RS are more than User Mining
○ HCI
○ Economical models
○ …
239. Xavier Amatriain – July 2014 – Recommender Systems
Conclusions
● RS are fairly new but already grounded on well-
proven technology
○ Collaborative Filtering
○ Machine Learning
○ Content Analysis
○ Social Network Analysis
○ …
● However, there are still many open questions
and a lot of interesting research to do!
240. Xavier Amatriain – July 2014 – Recommender Systems
Index
1. Introduction: What is a Recommender System
2. “Traditional” Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
3. Novel Methods
3.1. Learning to Rank
3.2. Context-aware Recommendations
3.2.1. Tensor Factorization
3.2.2. Factorization Machines
3.3. Deep Learning
3.4. Similarity
3.5. Social Recommendations
4. Hybrid Approaches
5. A practical example: Netflix
6. Conclusions
7. References
242. Xavier Amatriain – July 2014 – Recommender Systems
● "Recommender Systems Handbook." Ricci, Francesco, Lior Rokach,
Bracha Shapira, and Paul B. Kantor. (2010).
● “Recommender systems: an introduction”. Jannach, Dietmar, et al.
Cambridge University Press, 2010.
● “Toward the Next Generation of Recommender Systems: A Survey of the
State-of-the-Art and Possible Extensions”. G. Adomavicious and A.
Tuzhilin. 2005. IEEE Transactions on Knowledge and Data Engineering,
17 (6)
● “Item-based Collaborative Filtering Recommendation Algorithms”, B.
Sarwar et al. 2001. Proceedings of World Wide Web Conference.
● “Lessons from the Netflix Prize Challenge.”. R. M. Bell and Y. Koren.
SIGKDD Explor. Newsl., 9(2):75–79, December 2007.
● “Beyond algorithms: An HCI perspective on recommender systems”. K.
Swearingen and R. Sinha. In ACM SIGIR 2001 Workshop on
Recommender Systems
● “Recommender Systems in E-Commerce”. J. Ben Schafer et al. ACM
Conference on Electronic Commerce. 1999-
● “Introduction to Data Mining”, P. Tan et al. Addison Wesley. 2005
References
243. Xavier Amatriain – July 2014 – Recommender Systems
● “Evaluating collaborative filtering recommender systems”. J. L. Herlocker,
J. A. Konstan, L. G. Terveen, and J. T. Riedl. ACM Trans. Inf. Syst., 22(1):
5–53, 2004.
● “Trust in recommender systems”. J. O’Donovan and B. Smyth. In Proc. of
IUI ’05, 2005.
● “Content-based recommendation systems”. M. Pazzani and D. Billsus. In
The Adaptive Web, volume 4321. 2007.
● “Fast context-aware recommendations with factorization machines”. S.
Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. In Proc. of
the 34th ACM SIGIR, 2011.
● “Restricted Boltzmann machines for collaborative filtering”. R.
Salakhutdinov, A. Mnih, and G. E. Hinton.In Proc of ICML ’07, 2007
● “Learning to rank: From pairwise approach to listwise approach”. Z. Cao
and T. Liu. In In Proceedings of the 24th ICML, 2007.
● “Introduction to Data Mining”, P. Tan et al. Addison Wesley. 2005
References
244. Xavier Amatriain – July 2014 – Recommender Systems
● D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online
bayesian recommendations. In Proc.of the 18th WWW, 2009.
● Koren Y and J. Sill. OrdRec: an ordinal model for predicting personalized
item rating distributions. In Rec-Sys ’11, pages 117–124, 2011.
● Y. Koren. Factorization meets the neighborhood: a multifaceted
collaborative filtering model. In Proceedings of the 14th ACM SIGKDD,
2008.
● Yifan Hu, Y. Koren, and C. Volinsky. Collaborative Filtering for Implicit
Feedback Datasets. In Proc. Of the 2008 Eighth ICDM, pages 263–272,
2008.
● Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic.
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more
filtering. In Proc. of the sixth Recsys, 2012.
● Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson,A. Hanjalic, and N. Oliver.
TFMAP: optimizing MAP for top-n context-aware recommendation. In Proc.
Of the 35th SIGIR, 2012.
References
245. Xavier Amatriain – July 2014 – Recommender Systems
● A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse
recommendation: n-dimensional tensor factorization for context-aware
collaborative filtering. In Proc. of the fourth ACM Recsys, 2010.
● S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast
context-aware recommendations with factorization machines. In Proc. of
the 34th ACM SIGIR, 2011.
● S.H. Yang, B. Long, A.J. Smola, H. Zha, and Z. Zheng. Collaborative
competitive filtering: learning recommender using context of user choice. In
Proc. of the 34th ACM SIGIR, 2011.
● N. N. Liu, X. Meng, C. Liu, and Q. Yang. Wisdom of the better few: cold
start recommendation via representative based rating elicitation. In Proc. of
RecSys’11, 2011.
● M. Jamali and M. Ester. Trustwalker: a random walk model for combining
trust-based and item-based recommendation. In Proc. of KDD ’09, 2009.
References
246. Xavier Amatriain – July 2014 – Recommender Systems
● J. Noel, S. Sanner, K. Tran, P. Christen, L. Xie, E. V. Bonilla, E.
Abbasnejad, and N. Della Penna. New objective functions for social
collaborative filtering. In Proc. of WWW ’12, pages 859–868, 2012.
● X. Yang, H. Steck, Y. Guo, and Y. Liu. On top-k recommendation using
social networks. In Proc. of RecSys’12, 2012.
References