Issue No. 06 - November/December (2010 vol. 14)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIC.2010.104
Ana Belen Barragans Martinez , Centro Universitario del la Defensa en la Escuela Naval Militar de Marin, Spain
Marta Rey Lopez , Conselleria de Educacion e O.U., Spain
Enrique Costa Montenegro , University of Vigo, Spain
Fernando A. Mikic Fonte , University of Vigo, Spain
Juan C. Burguillo , University of Vigo, Spain
Ana Peleteiro , University of Vigo, Spain
Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take advantage of Web 2.0 applications, the authors propose using information obtained from social tagging to improve the recommendations. The Web 2.0 TV program recommender queveo.tv currently combines content-based and collaborative filtering techniques. This article presents a novel tag-based recommender to enhance the recommending engine by improving the coverage and diversity of the suggestions.
Internet computing, Web 2.0, recommendation systems, collaborative filtering, content-based filtering, folksonomy, tag-based recommenders
F. A. Mikic Fonte, M. Rey Lopez, E. Costa Montenegro, J. C. Burguillo, A. B. Barragans Martinez and A. Peleteiro, "Exploiting Social Tagging in a Web 2.0 Recommender System," in IEEE Internet Computing, vol. 14, no. , pp. 23-30, 2010.