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.