On the enhancement of collaborative filtering by demographic data

Demographic data regarding users and items exist in most available recommender systems data sets. Still, there has been limited research involving such data. This work sets the foundations for a novel filtering technique which relies on information of that kind. It starts by providing a general, step-by-step description of an approach which combines demographic information with existing filtering algorithms, via a weighted sum, in order to generate more accurate predictions. U-Demog and I-Demog are presented as an application of that general approach specifically on User-based and Item-based Collaborative Filtering. Several experiments involving different settings of the proposed approach support its utility and prove that it shows enough promise in generating predictions of improved quality.