Content-based recommendation in social tagging systems

We present and evaluate various content-based recommendation models that make use of user and item profiles defined in terms of weighted lists of social tags. The studied approaches are adaptations of the Vector Space and Okapi BM25 information retrieval models. We empirically compare the recommenders using two datasets obtained from Delicious and Last.fm social systems, in order to analyse the performance of the approaches in scenarios with different domains and tagging behaviours. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval

• Type: Conference
• Year: 2010
• Where: RECSYS
• Authors: Iván Cantador, Alejandro Bellogín, David Vallet
• Keywords: Content-based Recommendation Models, Control Systems, Information Retrieval Models, Last.fm Social Systems, RECSYS 2010