Title: On collaborative filtering model optimised with multi-item attribute information space for enhanced recommendation accuracy
Authors: Folasade O. Isinkaye; Yetunde O. Folajimi; Adesesan B. Adeyemo
Addresses: Department of Computer Science, Ekiti State University, Ado-Ekiti, Nigeria ' Playable Innovation Technology (PlaIT) Laboratory, Northeastern University, Boston, MA, USA ' Department of Computer Science, University of Ibadan, Nigeria
Abstract: Recommender system is a type of information filtering system that is designed to curtail the difficulties of information overload by automatically suggesting relevant items to users tailored to their preferences. Bayesian personalised smart linear methods (BPRSLIM) is a variant of item-based collaborative filtering technique used in information filtering system. Although, this algorithm has shown outstanding performance in a range of applications, nevertheless it suffers serious limitation of inability to provide accurate and reliable recommendations when the user-item matrix contains insufficient rating information, this always reduces its accuracy. In this paper, we propose a framework that integrates multi-item attribute information besides the classic information of users and items into BPRSLIM model in order to ease the sparsity problem associated with it and hence improves its performance accuracy. The enhanced model is expected to outperform the original BPRSLIM model.
Keywords: Bayesian personalised smart linear methods; BPRSLIM; sparsity problem; recommender system; collaborative filtering; item attribute information; optimisation.
International Journal of Intelligent Systems Technologies and Applications, 2020 Vol.19 No.3, pp.207 - 215
Received: 10 Apr 2018
Accepted: 31 Oct 2018
Published online: 02 Jul 2020 *