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
The social bookmark and publication management system bibsonomy
The VLDB Journal — The International Journal on Very Large Data Bases
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Users of social tagging systems spontaneously annotate resources providing, in this way, useful information about their interests. A collaborative filtering recommender system can use this feedback in order to identify people and resources more strictly related to a specific topic of interest. Such a collaborative filtering approach can compute similarities among tags in order to select resources associated to tags relevant for a specific interest of the user. Several research works try to infer these similarities by evaluating co-occurrences of tags over the entire set of annotated resources discarding, in this way, information about the personal classification provided by users. This paper, on the other hand, proposes an approach aimed at observing only the set of annotations of a single user in order to identify his topic of interests and to produce personalized recommendations. More specifically, following the idea that each user may have several distinct interests and people may share just some of these interests, our approach adaptively filters and combines the feedback of users according to a specific topic of interest of a user.