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Evaluating collaborative filtering recommender systems
TLDR
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. Expand
An algorithmic framework for performing collaborative filtering
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GroupLens: applying collaborative filtering to Usenet news
TLDR
The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high. Expand
Explaining collaborative filtering recommendations
TLDR
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. Expand
Collaborative Filtering Recommender Systems
TLDR
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. Expand
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms
TLDR
An analysis framework is applied that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component, and identifies the three components identified are similarity computation, neighbor selection, and rating combination. Expand
Combining Collaborative Filtering with Personal Agents for Better Recommendations
TLDR
This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone. Expand
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system
TLDR
The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques and is experimentally validated by showing that even simple filterbots such as spell checking can increase the utility for users of sparsely populated collaborative filtering system. Expand
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
TLDR
It is empirically demonstrated that two of the most acclaimed CF recommendation algorithms have flaws that result in a dramatically unacceptable user experience, and a new Belief Distribution Algorithm is introduced that overcomes these flaws and provides substantially richer user modeling. Expand
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