From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to performcollaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from localcommunity detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt onestate-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that theprecision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almostoffsets the absence of ratings by exploiting a reduced set of mentors