Predicting social-tags for cold start book recommendations

Sharon Givon, Victor Lavrenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We demonstrate how user ratings can be accurately predicted from a set of tags assigned to a book on a social-networking site. Since a newly-published book is unlikely to have social-tags already assigned to it, we describe a probabilistic model for inferring the most probable tags from the text of the book. We evaluate the proposed approach on a newly-created corpus, involving 146 books and 1060 users. Our experiments demonstrate that the proposed approach is significantly better than a well-tuned collaborative filtering baseline for books with 10 or fewer ratings. We also show how predictions based on social-tags can be combined with the traditional collaborative-filtering methods to yield superior performance with any number of ratings.
Original languageEnglish
Title of host publicationProceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, New York, NY, USA, October 23-25, 2009
Number of pages4
Publication statusPublished - Oct 2009


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