Leveraging aggregate ratings for improving predictive performance of recommender systems

Abstract

This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of recommender systems, including two types of collaborating filtering and a hierarchical linear regression model. First, we present a framework for incorporating aggregate rating information and apply this framework to the aforementioned individual rating models. Then we formally show that this additional aggregate rating information provides more accurate recommendations of individual items to individual users. Further, we experimentally confirm this theoretical finding by demonstrating on several datasets that the aggregate rating information indeed leads to better predictions of unknown ratings. We also propose scalable methods for incorporating this aggregate information and test our approaches on large datasets. Finally, we demonstrate that the aggregate rating information can also be used as a solution to the cold start problem of recommender systems.