On the enhancement of collaborative filtering by demographic data

Article type: Research Article

Authors: Vozalis, Manolis | Margaritis, Konstantinos G.

Affiliations: Parallel Distributed Processing Laboratory, Department of Applied Informatics, University of Macedonia, Egnatia 156, PO Box 1591, 54006, Thessaloniki, Greece

Note: [] Corresponding author. E-mail: [email protected]

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.

Keywords: Collaborative filtering, memory-based filtering, demographic data, personalization, prediction, recommender systems

Journal: Web Intelligence and Agent Systems: An international journal, vol. 4, no. 2, pp. 117-138, 2006