Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Supporting Personalized User Concept Spaces and Recommendations for a Publication Sharing System
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Collaborative filtering recommender systems
The adaptive web
Journal of the American Society for Information Science and Technology
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User-based Collaborative Filtering (CF) systems generate recommendations for a specific user by combining feedback (i.e. information about what is relevant for a user) provided by a set of people similar to that user. In these system the similarity among people is computed by taking into account the set of shared resources. However, there are several application domains, such as social tagging systems, where each user may have several different Topic of Interests (ToIs). In these cases, two users could share only some interests and, therefore, only a part of the feedback should be considered for producing recommendations. Focusing on social tagging systems, we propose here a novel approach to detect ToIs in the collection of the bookmarks of a user. Given a specific ToI, we adaptively identify similar people (i.e., sharing the same ToI) and select only the resources relevant to the specific ToI.