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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.