Expert based prediction of user preferences

Two influential strands in Recommender systems (RS) are the collaborative filtering and content based filtering that by taking into account user communities or interaction history suggest to the active user interesting items. However, the aforementioned approaches do not work well when confronted with new users with few interactions; or with the addition of new items. In such cases, the guidance of an expert could help the active user. In this paper we provide a definition of expert users that can be reduced into two components the expertise and the contribution. The former is related to the content of items evaluated by an expert and the latter refers to the influence of the expert to the users of a RS. In particular, contribution is learnt with the aid of a perceptron. Experts users are defined for values of the features of the items. Furthermore, we have studied the temporal evolution of the experts, as new users, new items, or new item evaluations are added into the system. Moreover, we have compared the proposed expert based method with a stereotype based method, since for both methods a minimal interaction of the active user with the RS suffices. The data originated from the MovieLens set with enhancements from the IMDB.