Authors
Marco Degemmis, Pasquale Lops, Giovanni Semeraro
Publication date
2007/7
Journal
User Modeling and User-Adapted Interaction
Volume
17
Pages
217-255
Publisher
Kluwer Academic Publishers
Description
Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are …
Total citations
Scholar articles
M Degemmis, P Lops, G Semeraro - User Modeling and User-Adapted Interaction, 2007