An analytic approach to separate users by introducing new combinations of initial centers of clustering

In the recommender systems, the users evaluate and rate data items and assign quantitative indices to them; consequently, a comprehensive database called rating matrix is created. In the rating matrix, the separation of the gray sheep users is of crucial importance since these users increase the error rate of the recommendations to the white users. The proposed approach involved three main steps. In the first step, we introduced a new algorithm called MKMeans++ by extracting and combining some features of the rating matrix as well as applying these combinations to the initial centers of the KMeans clustering, in which Power Item and Power Weight features were added to the new combinations. These features indicated the superiority of the proposed algorithm over the KMeans++ algorithm. In the second step, we probed into the relationship between features of the rating matrix and the gray sheep users, the results of which revealed that the gray sheep users can be identifiable only through the Distance feature. In the third step, we started separating the gray sheep users by using MKMeans++ algorithm and applying the Distance feature. Experimental results on the MovieLens and FilmTrust datasets show that the proposed algorithm outperforms existing methods in terms of the MAE accuracy and Converge. In the proposed algorithm, on average, the amount of the MAE improvement for MovieLens and FilmTrust datasets were 0.15 and 0.24, respectively, while also increasing the quality of clusters and the precision of the recommender system.