Exploiting Social Tagging in a Web 2.0 Recommender System

Authors:
Ana Belen Barragans Martinez;Marta Rey Lopez;Enrique Costa Montenegro;Fernando A. Mikic Fonte;Juan C. Burguillo;Ana Peleteiro
Affiliations:
Centro Universitario del la Defensa en la Escuela Naval Militar de Marin, Spain;Conselleria de Educacion e O.U., Spain;University of Vigo, Spain;University of Vigo, Spain;University of Vigo, Spain;University of Vigo, Spain
Venue:
IEEE Internet Computing
Year:
2010

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Abstract

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.