Effect of dataset size on efficiency of collaborative filtering recommender systems with multi-clustering as a neighbourhood identification strategy

Abstract

Determination of accurate neighbourhood of an active user (a user to whom recommendations are generated) is one of the essential problems that collaborative filtering based recommender systems encounter. Properly adjusted neighbourhood leads to more accurate recommendation generated by a recommender system. In classical collaborative filtering technique, the neighbourhood is modelled by kNN algorithm, but this approach has poor scalability. Clustering techniques, although improved time efficiency of recommender systems, can negatively affect the quality (precision or accuracy) of recommendations. This article presents a new approach to collaborative filtering recommender systems that focuses on the problem of an active user’s neighbourhood modelling. Instead of one clustering scheme, it works on a set of partitions, therefore it selects the most appropriate one that models the neighbourhood precisely. This article presents the results of the experiments validating the advantage of multi-clustering approach, $$M-CCF$$, over the traditional methods based on single-scheme clustering. The experiments particularly focus on the effect of great size of datasets concerning overall recommendation performance including accuracy and coverage.