An Experimental Study in Cross-Representation Mediation of User Models

Abstract

The paper presents the result on cross-representation mediation of user models in the context of movie recommendation. We analyze the possibility of initializing the user models for a content-based recommender starting from movie ratings provided by users in other social applications. We focus in particular on (i) an approach for inferring user model preferences from rating and (ii) the experimentation of several methods to solve the missing value problem exploiting community-based ratings. We tested different variations of the proposed approach exploiting a subset of the MovieLens 10M Dataset, computing rating predictions, and MAE.