Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Demographic prediction based on user's browsing behavior
Proceedings of the 16th international conference on World Wide Web
Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach
International Journal of Electronic Commerce
A continuous weighted low-rank approximation for collaborative filtering problems
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Intelligent search on the internet
Reasoning, Action and Interaction in AI Theories and Systems
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
A latent model for collaborative filtering
International Journal of Approximate Reasoning
Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Future Generation Computer Systems
Transfer learning in heterogeneous collaborative filtering domains
Artificial Intelligence
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning in probabilistic graphs exploiting language-constrained patterns
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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As the information on the web increases exponentially, so do the efforts to automatically filter out useless content and to search for interesting content. Through both explicit and implicit actions, users define where their interests lie. Recent efforts have tried to group similar users together in order to better use this data to provide the best overall filtering capabilities to everyone. This thesis discusses ways in which linear algebra, specifically the singular value decomposition, can be used to augment these filtering capabilities to provide better user feedback. The goal is to modify the way users are compared with one another, so that we can more efficiently predict similar users. Using data collected from the PhDs.org website, we tested our hypothesis on both explicit web page ratings and implicit visits data.