Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Recommender System Based on Consumer Product Reviews
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Preference-based graphic models for collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Book recommender prototype based on author's writing style
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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In addition to using the many conventional approaches for making recommendation systems, this paper proposes a complementary recommendation methodology. It is focused on book recommendation. It proposes to make a comprehensive data repository of existing books, available at the level of the user (in a library or a personal bookshelf). Further, by retrieving web reviews of available books and new books by using existing web services, an infrastructure has been developed for need-based book recommendation system. Implementation results show that our book recommendation allows a user to eliminate irrelevant books and presents the desired books to the user from given book set. The proposed book recommender is one of the first systems in terms of focusing on meeting individuals' needs rather than calculating similarity or preferences automatically, which is adopted by the traditional recommender system.