GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Modern Information Retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
SIFT: a tool for wide-area information dissemination
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
A method for weighting multi-valued features in content-based filtering
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Hybrid recommendation based on low-dimensional augmentation of combined feature profiles
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Expert Systems with Applications: An International Journal
An ontology-based IT student model in an educational social network
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Recommender systems are gaining widespread acceptance in e-commerce applications to confront the "information overload" problem. Collaborative Filtering (CF) is a successful recommendation technique, which is based on past ratings of users with similar preferences. In contrast, Content-Based filtering (CB) assumes that each user operates independently. As a result, it exploits only information derived from document or item features. Both approaches have been extensively combined to improve the recommendation procedure. Most of these systems are hybrid: they run CF on the results of CB and vice versa. CF exploits information from the users and their ratings. CB exploits information from items and their features. In this paper, we construct a feature-weighted user profile to disclose the duality between users and features. Exploiting the correlation between users and features we reveal the real reasons of their rating behavior. We perform experimental comparison of the proposed method against the well-known CF, CB and a hybrid algorithm with a real data set. Our results show significant improvements, in terms of effectiveness.