A probabilistic model for recommending to new cold-start non-registered users

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• 作者：Antonio Hernando^a ; ^{ahernando@etsisi.upm.es" class="auth_mail" title="E-mail the corresponding author} ; Jesú ; s Bobadilla^a ; ^{jesus.bobadilla@upm.es" class="auth_mail" title="E-mail the corresponding author} ; Fernando Ortega^b ; ^{fernando@bigtruedata.com" class="auth_mail" title="E-mail the corresponding author} ; Abraham Gutié ; rrez^a ; ^{abraham@etsisi.upm.es" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Recommender systems ; Collaborative filtering ; Graphical probabilistic models
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：10 January 2017
• 年：2017
• 卷：376
• 期：Complete
• 页码：216-232
• 全文大小：1129 K

文摘

Recommender Systems are designed to provide recommendations to registered users. Non-registered 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.