Book ChapterDOI
PolyLens: a recommender system for groups of users
Mark O'Connor,Dan Cosley,Joseph A. Konstan,John Riedl +3 more
- pp 199-218
TLDR
An analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations.Abstract:
We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via emailread more
Citations
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Journal ArticleDOI
The MovieLens Datasets: History and Context
TL;DR: The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.
Book ChapterDOI
Collaborative filtering recommender systems
TL;DR: This chapter introduces the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings.
Journal ArticleDOI
Recommender system application developments
TL;DR: This paper reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories, and summarizes the related recommendation techniques used in each category.
Journal ArticleDOI
Collaborative Filtering Recommender Systems
TL;DR: A wide variety of the choices available and their implications are discussed, aiming to provide both practicioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
Journal ArticleDOI
Recommender systems: from algorithms to user experience
Joseph A. Konstan,John Riedl +1 more
TL;DR: It is argued that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and additional measures that have proven effective are suggested.
References
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Proceedings ArticleDOI
GroupLens: an open architecture for collaborative filtering of netnews
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
Journal ArticleDOI
Recommender systems
Paul Resnick,Hal R. Varian +1 more
TL;DR: This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.
Proceedings ArticleDOI
Social information filtering: algorithms for automating “word of mouth”
Upendra Shardanand,Pattie Maes +1 more
TL;DR: The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.