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Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
A classification-based review recommender
Knowledge-Based Systems
Towards text-based recommendations
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Proceedings of the fifth ACM conference on Recommender systems
Will I Like It? Providing Product Overviews Based on Opinion Excerpts
CEC '11 Proceedings of the 2011 IEEE 13th Conference on Commerce and Enterprise Computing
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system
Proceedings of the sixth ACM conference on Recommender systems
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Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users' review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders.