An application of fuzzy geographically clustering for solving the Cold-Start problem in recommender systems
In this paper, we present a novel method based on fuzzy geographically clustering to solve the Cold-Start problem in Recommender Systems occurring when a new user is migrated into the system. The proposed method can handle the issues of selected demographic attributes, the similarities between items and missing ratings that existed in relevant demographic-based algorithms. Numerical examples are given to illustrate the proposed method. Experimental results show that the new method has better accuracy than other relevant ones.