Predicting social-tags for cold start book recommendations

ABSTRACT

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.

REFERENCES

• 1: D. Eck, T. Bertin-Mahieux, and P. Lamere. Autotagging music using supervised machine learning. In Proceedings of The 8th International Conference on Music Information Retrieval, 2007.
• 2: S. Givon and V. Lavrenko. Large scale book annotation with social tags. In International Conference on Weblogs and Social Media, San Jose, CA, 2009.
• 3: S. Givon and T. Wilson. An automatic classification of book texts to user-defined tags. In Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008), Seattle, WA, USA, 2008. AAAI Press.
• 4: J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 119--126, New York, NY, USA, 2003. ACM.
• 5: J. A. Konstan, J. Riedl, A. Borchers, and J. Helocker. Recommender systems: A grouplens perspective. In Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08), pages 60--64. AAAI Press, 1998.
• 6: V. Lavrenko and W. Croft. Relevance-based language models. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 120--127, New York, NY, USA, 2001. ACM.
• 7: G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, January 2003.
• 8: R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries, pages 195--204. ACM Press, 1999.
• 9: X. Yi, J. Allan, and V. Lavrenko. Discovering missing values in semi-structured databases. In Proceedings of RIAO 2007 - 8th Conference - Large-Scale Semantic Access to Content (Text, Image, Video and Sound), 2006. electronic proceedings only.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services

General Terms:
Algorithms, Measurement

Keywords:
annotation, recommender systems, social-tags