Title: Exploring graph-based global similarity estimates for quality recommendations

Authors: Deepa Anand; Kamal K. Bharadwaj

Addresses: Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India ' School of Computer and System Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

Abstract: Data sparsity or the insufficiency of past user preferences in predicting future user needs continues to be a major challenge for RS engines. We propose a solution to the sparsity problem by exploring similarity measures that capture the global patterns of commonality between users or items by leveraging on indirect ways of connecting users (items) through a user (item) graph. Entities (users or items) sharing common features are connected to each other by edges weighted by their proximity or distance. Graph-based techniques, for estimating transitive similarity between entities not directly connected, are exploited to bring the entities closer thus facilitating collaboration. Furthermore, we also propose a combined user-item graph approach for exploiting the similarity between users preferring similar items (and vice versa). In this work, we have suggested alternatives to the already existing global similarity assessment and we aim to investigate the appropriateness of the proposed techniques under differing data features.

Keywords: recommender systems; collaborative filtering; global similarity; user graphs; item graphs; max flow min cut; distinct paths; computational intelligence; quality recommendations; user preferences; user needs.

DOI: 10.1504/IJCSE.2014.060683

International Journal of Computational Science and Engineering, 2014 Vol.9 No.3, pp.188 - 197

Received: 23 Jan 2012
Accepted: 02 Jul 2012

Published online: 24 May 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article