Identifying the effects of SVD and demographic data use on generalized collaborative filtering

Abstract

The purpose of this paper is to examine how singular value decomposition (SVD) and demographic information can improve the performance of plain collaborative filtering (CF) algorithms. After a brief introduction to SVD, where the method is explained and some of its applications in recommender systems are detailed, we focus on the proposed technique. Our approach applies SVD in different stages of an algorithm, which can be described as CF enhanced by demographic data. The results of a rather long series of experiments, where the proposed algorithm is successfully blended with user- and item-based CF, show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of recommender systems, but also assists in increasing the accuracy of systems employing it.

Keywords:

• recommender systems
• collaborative filtering
• personalization
• singular value decomposition (SVD)
• demographic data

CCS Categories :

• I.2.6 (artificial intelligence): learning – parameter learning
• H.3.3 (information storage and retrieval): information search and retrieval – clustering
• information filtering

Acknowledgements

The authors wish to thank the anonymous reviewers for their valuable suggestions.