Efficient Distributed MST Based Clustering for Recommender Systems

Abstract

This paper presents the Distributed Kruskal Algorithm for Minimum Spanning Tree (MST) based clustering to be used in the context of recommendation engines. The algorithm can operate over large graph data sets distributed over a number of machines. The operation of the algorithm is evaluated by comparing both the quality of the cluster configurations produced, and the accuracy of the predictions, with non-MST based clustering approaches. The results indicate that the proposed approach produces comparable recommendations at much lower storage, hence runtime, costs.