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Many websites use tags as a mechanism for improving item metadata through collective user effort. Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. In this paper we explore tag selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.
Sen et al. (Sun,) studied this question.
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