Augmenting Item Exposure in Collaborative Filtering
Date Issued
2015
Date
2015
Author(s)
Shih, Ting-Yi
Abstract
New items, e.g., mobile apps and movies, have been growing so fast that most of them cannot get discovered in a recommendation system. We propose a two-stage approach to appropriately promote new items. Different from pre- vious works on Collaborative Filtering (CF), our approach is not based only on item quality or user satisfaction. We force the new items to be promoted to those who would be potentially able to give ratings, and then leverage the gathered user preference to punish the promoted items with low quality in- trinsically. By slightly sacrificing the benefit of recommending the best items in terms of item quality or user satisfaction, our solution seeks to provide all of the items with a chance to be visible equally. The result of the experiments conducted on MovieLens and Netflix data demonstrates the feasibility of the approach.
Subjects
recommendation
recommendation system
item exposure
exposure
collaborative filtering
cold start
Type
thesis
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