The Performance of Total Variation Regularizer for User Collaborative Filtering

Abstract

Recommender systems (RS) assist users in making decisions by filtering content that the user would likely find relevant. Standard techniques like collaborative filtering exploit user similarities to find the recommendations assuming that similar users are likely to be interested in the same items. On the other hand, graph RS borrow techniques from the field of graph signal processing to predict ratings for items that the users have not seen and utilize a graph representation of user similarities and their corresponding ratings. Recent studies indicated that simple RS could outperform the state-of-the-art RS. Therefore our paper contributes by analyzing the performance of a novel approach to RS - collaborative filtering that uses a total variation graph regularizer. We show that total variation can outperform its predecessor collaborative filtering by reducing the root mean squared error by 4.68%. However, further experiments with top-n recommendations indicated that traditional collaborative filtering could recommend more relevant content than the total variation, which is supported by a 2.20% increase in precision. The results indicate that total variation alone does not add more information to the RS to show noteworthy improvements to existing traditional baselines. However, building on discoveries that even some of the state-of-the-art RS cannot outperform well-defined baselines, a 4.68% increase in accuracy reveals the potential of RS supported by graph regularization.