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Published in:
2013 | OriginalPaper | Chapter
Improving recommendation accuracy based on item-specific tag preferences
Author : Dr. Fatih Gedikli
Published in: Recommender Systems and the Social Web
Publisher: Springer Fachmedien Wiesbaden
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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 (
affect
) 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.