Multi-Clustering Applied to Collaborative Recommender Systems

Abstract

This article discusses clustering approach to recommender systems acceleration and presents application of multi-clustering algorithms in the recommender systems based on collaborative filtering. It is explained the motivation for multi-clustering usage in comparison to clustering techniques, as well as results of experiments. Multi-clustering is variously defined in literature, however the common issue is its multiple views of one dataset. Different views may represent distinct aspects of the same data, adapting the most appropriate one to the current problem. In recommender systems domain it can be applied as a tool for precise modelling neighbourhood of object the recommendations are generated to. This article presents results of experiments demonstrating multi-clustering advantage over traditional clustering in neighbourhood determination.