HU-FCF++: A novel hybrid method for the new user cold-start problem in recommender systems

Abstract

Recommender system (RS) is a special type of information systems that assists decision makers to choose appropriate items according to their preferences and interests. It is utilized in different domains to personalize its applications by recommending items, such as books, movies, songs, restaurants, news articles, jokes, among others. An important issue in RS namely the new user cold-start problem occurring when a new user migrates to the system has grasped a great attraction of researchers in recent years. Existing researches are faced with the limitations of the relied dataset, the determination of the optimal number of clusters, the similarity metric, irrelevant users and the selection of membership values. In this paper, we present a novel hybrid method so-called HU-FCF++ to deal with these drawbacks by considering the integration of existing state-of-the-arts of several groups of methods in order to combine the advantages of different groups and eliminate their disadvantages by some special procedures. A numerical example on a simulated dataset is given to illustrate the activities of the proposed approach. Experimental validation on the benchmark RS datasets show that HU-FCF++ achieves better accuracy than the relevant methods.