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Ranking and Suggesting Popular Items

Platform : Web Mining.J2EE

IEEE Projects Years : 2009

We consider the problem of ranking and suggesting popular items based on user feedback that appears in applications such as social tagging and search query suggestions. In particular, we assume that the user feedback is generated as follows. The system suggests to each user a (small) subset of items from the set of all possible items. The user can then choose an item from her suggestion set, or alternatively choose an item from the set of all possible items. Using this feedback, the goal is to quickly learn the true popularity of items, and hence being able to suggest items to users that are indeed popular. The difficulty that arises in this context is that making suggestions to users can reinforce the popularity of some items, and hence distort the resulting item ranking. In this paper, we provide an analysis of this problem. We first formally show that suggesting items to users can indeed lead to a skewed popularity ranking of items. We then propose several algorithms for ranking and suggesting items, and study their performance. In addition, we illustrate our results using a numerical cased study that is based on the inferred popularity of tags from a month-long crawl of a popular social bookmarking service. While ”na¨ıve” algorithms can lead to a skewed ranking, our results suggests that there exist simple algorithms for ranking and suggesting items that lead to good performance.

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