Journal
INFORMATION SCIENCES
Volume 376, Issue -, Pages 216-232Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.10.009
Keywords
Recommender systems; Collaborative filtering; Graphical probabilistic models
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Funding
- Spanish Ministerio de Economia y Competitividad project [TIN2012-32682]
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Recommender Systems are designed to provide recommendations to registered users. Nonregistered users can be regarded as a particular case of the pure new user cold-start problem. Since non-registered users have neither created a profile account nor rated any item, recommender systems cannot know the tastes of non-registered users, and they typically provide these non-registered users with the average rating of each item. Nevertheless, non-registered users are an important proportion of users of many recommender systems. Therefore, more sophisticated ways of recommending to these non-registered users are wished. Here, we will propose to offer these non-registered users a natural inference model based on uncertainty rules that allows them to infer themselves their own recommendations. This is mathematically formalized by means of a probabilistic model that simulates the forward reasoning based on rules. (C) 2016 Elsevier Inc. All rights reserved.
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