Applying SVD on item-based filtering

Abstract

In this paper we examine the use of a matrix factorization technique called singular value decomposition (SVD) in item-based collaborative filtering. After a brief introduction to SVD and some of its previous applications in recommender systems, we proceed with a full description of our algorithm, which uses SVD in order to reduce the dimension of the active item's neighborhood. The experimental part of this work first locates the ideal parameter settings for the algorithm, and concludes by contrasting it with plain item-based filtering which utilizes the original, high dimensional neighborhood. The results show that a reduction in the dimension of the item neighborhood 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.