On the enhancement of collaborative filtering by demographic data

Authors:
Manolis Vozalis;Konstantinos G. Margaritis
Affiliations:
Parallel Distributed Processing Laboratory, Department of Applied Informatics, University of Macedonia, Egnatia 156, PO Box 1591, 54006, Thessaloniki, Greece;Parallel Distributed Processing Laboratory, Department of Applied Informatics, University of Macedonia, Egnatia 156, PO Box 1591, 54006, Thessaloniki, Greece
Venue:
Web Intelligence and Agent Systems
Year:
2006

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Abstract

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.