Book ChapterDOI
Generating predictive movie recommendations from trust in social networks
Jennifer Golbeck
- pp 93-104
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TLDR
This paper presents FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations, and shows that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average.Abstract:
Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types.read more
Citations
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Book ChapterDOI
Introduction to Recommender Systems Handbook
TL;DR: The main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.
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.
Book ChapterDOI
Recommender Systems: Introduction and Challenges
TL;DR: This introductory chapter briefly discusses basic RS ideas and concepts and aims to delineate, in a coherent and structured way, the chapters included in this handbook.
Journal ArticleDOI
The Reader-to-Leader Framework: Motivating Technology-Mediated Social Participation
Jennifer Preece,Ben Shneiderman +1 more
TL;DR: In this article, the Reader-to-Leader Framework is proposed to understand what motivates technology-mediated social participation and improve user interface design and social support for companies, government agencies, and non-governmental organizations.
Journal ArticleDOI
Recommender Systems
TL;DR: In this article, a review of recent developments in recommender systems and discuss the major challenges and major challenges of recommender system and their potential impacts and future directions are discussed, and they compare and evaluate available algorithms and examine their roles in the future developments.
References
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Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
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
Explaining collaborative filtering recommendations
TL;DR: This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Proceedings ArticleDOI
Supporting trust in virtual communities
A Abdul-Rahman,Stephen Hailes +1 more
TL;DR: In this article, a trust model that is grounded in real-world social trust characteristics, and based on a reputation mechanism, or word-of-mouth, is proposed for the virtual medium.
Dissertation
Computing and applying trust in web-based social networks
TL;DR: It is shown that, in the case where the user's opinion is divergent from the average, the trust-based recommended ratings are more accurate than several other common collaborative filtering techniques.