Dynamic recommender system : using cluster-based biases to improve the accuracy of the predictions

TL;DR

This paper introduces a cluster-based matrix factorization method for recommender systems that allows online updating of predictions, addressing the static nature of traditional models and improving accuracy between full re-factorizations.

Contribution

It proposes a novel cluster-based bias integration technique enabling real-time updates in matrix factorization models for better prediction accuracy.

Findings

Enhanced prediction accuracy between re-factorizations

Effective online integration of new ratings

Validated on large datasets

Abstract

It is today accepted that matrix factorization models allow a high quality of rating prediction in recommender systems. However, a major drawback of matrix factorization is its static nature that results in a progressive declining of the accuracy of the predictions after each factorization. This is due to the fact that the new obtained ratings are not taken into account until a new factorization is computed, which can not be done very often because of the high cost of matrix factorization. In this paper, aiming at improving the accuracy of recommender systems, we propose a cluster-based matrix factorization technique that enables online integration of new ratings. Thus, we significantly enhance the obtained predictions between two matrix factorizations. We use finer-grained user biases by clustering similar items into groups, and allocating in these groups a bias to each user. The experiments we did on large datasets demonstrated the efficiency of our approach.