Robust collaborative filtering

The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit.

Robust statistics is an area within statistics where estimation methods have been developed that deteriorate more gracefully in the presence of unmodeled noise and slight departures from modeling assumptions. In this work, we study how such robust statistical methods, in particular M-estimators, can be used to generate stable recommendation even in the presence of noise and spam. To that extent, we present a Robust Matrix Factorization algorithm and study its stability. We conclude that M-estimators do not add significant stability to recommendation; however the presented algorithm can outperform existing recommendation algorithms in its recommendation quality.