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.NYU, Stern School of Business, Center for Digital Economy Researc