Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems

Abstract

To offer an appropriate recommendation to customers in recommender systems, the issue of clustering and separating users with different tastes from the rest of people is of significant importance. The MkMeans\u2009+\u2009\u2009+\u2009algorithm is a technique for clustering and separating users in collaborative filtering systems. This algorithm utilizes a specific procedure for selecting the initial centroids of the clusters and has a better function compared with its similar algorithms such as kMeans\u2009+\u2009\u2009+\u2009. In this paper, MkMeans\u2009+\u2009\u2009+\u2009algorithm is combined with Firefly, Cuckoo, and Krill algorithms and new algorithms called FireflyMkMeans\u2009+\u2009\u2009+\u2009, CuckooMkMeans\u2009+\u2009\u2009+\u2009, and KrillMkMeans\u2009+\u2009\u2009+\u2009are introduced in order to specify the optimal centroid of the cluster, better separate users, and avoid local optimals. In the proposed hybrid clustering approach, the initial population of firefly, cuckoo, and krill algorithms is initialized through the solutions generated by MkMeans\u2009+\u2009\u2009+\u2009algorithm, and it makes use of the benefits of MkMeans\u2009+\u2009\u2009+\u2009as well as firefly, cuckoo, and krill algorithms. Results and implementations on both MovieLens and FilmTrust datasets indicate that the proposed algorithms can perform better than their similar algorithms in clustering and separating users with different tastes (graysheep users), and enhance the quality of clusters and the accuracy of recommendations for users with similar tastes (white users).