An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
kNN CF: a temporal social network
Proceedings of the 2008 ACM conference on Recommender systems
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Dynamic updating of online recommender systems via feed-forward controllers
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Improving large-scale search engines with semantic annotations
Expert Systems with Applications: An International Journal
Proceedings of the 7th ACM conference on Recommender systems
A novel Bayesian similarity measure for recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Recommendation systems, based on collaborative filtering, offer a means of sifting through the enourmous amounts of content on the web by composing user ratings in order to generate predicted ratings for other users. These kinds of systems can be viewed as a network of interacting peers, where each user is a node and the links to all other nodes are weighted according to how similar the corresponding users are. Predicted ratings are generated for a user for unknown items by requesting and aggregating rating information from the surrounding neighbors. However, the different methods of computing user similarity, or weighting the network links, very often do not agree with each other, and, as a result, the structure of the network of recommenders changes completely. In this work we perform an analysis of a range of similarity measures, comparing their performance in terms of prediction accuracy and coverage. This allows us to understand the effect that similarity measures have on predicted ratings. Based on the obtained results, we argue that user-similarity may not sufficiently capture the relationships that recommenders could otherwise share in order to maximise the utility of these communities.