Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Discovering missing values in semi-structured databases
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Collaborative filtering in social tagging systems based on joint item-tag recommendations
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Latent subject-centered modeling of collaborative tagging: An application in social search
ACM Transactions on Management Information Systems (TMIS)
Personalized book recommendations created by using social media data
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Journal of the American Society for Information Science and Technology
What to read next?: making personalized book recommendations for K-12 users
Proceedings of the 7th ACM conference on Recommender systems
Measuring and addressing the impact of cold start on associative tag recommenders
Proceedings of the 19th Brazilian symposium on Multimedia and the web
| Hi-index | 0.00 |
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.