Recent research has indicated that “attaching feelings to tags” is experienced by users as a valuable means to express which features of an item they particularly like or dislike [Vig et al., 2010]. When following such an approach, users would therefore not only add tags to an item as in usual Web 2.0 applications, but also attach a preference (
) to the tag itself, expressing, for example, whether or not they liked a certain actor in a given movie. In this chapter, we show how this additional preference data can be exploited by a recommender system to make more accurate predictions.
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