A probabilistic model for recommending to new cold start non registered users
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.
Inf. Sci.