Abstract

Early in the development of recommender systems, hierarchical models were recognized as a tool capable of combining content-based filtering (recommending based on item-specific attributes) with collaborative filtering (recommending based on preferences of similar users). However, as recently as the late 2000s, many authors deemed the computational costs required to fit hierarchical models to be prohibitively high for commercial-scale settings. This talk addresses the challenge of fitting a hierarchical model at commercial scale by proposing a moment-based procedure for estimating the parameters of a hierarchical model. This procedure has its roots in a method originally introduced by Cochran in 1937. The method trades statistical efficiency for computational efficiency. It gives consistent parameter estimates, competitive prediction error performance, and substantial computational improvements. When applied to a large-scale recommender system application and compared to a standard maximum likelihood procedure, the method delivers competitive prediction performance while reducing computation time from hours to minutes.