Int. J. Intelligent Systems Technologies and Applications, Vol. 19, No. 3, 2020 207 Copyright © 2020 Inderscience Enterprises Ltd. On collaborative filtering model optimised with multi-item attribute information space for enhanced recommendation accuracy Folasade O. Isinkaye* Department of Computer Science, Ekiti State University, Ado-Ekiti, Nigeria Email: sadeisinkaye@gmail.com *Corresponding author Yetunde O. Folajimi Playable Innovation Technology (PlaIT) Laboratory, Northeastern University, Boston, MA, USA Email: yetundeofolajimi@gmail.com Adesesan B. Adeyemo Department of Computer Science, University of Ibadan, Nigeria Email: sesanadeyemo@gmail.com 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. Reference to this paper should be made as follows: Isinkaye, F.O., Folajimi, Y.O. and Adeyemo, A.B. (2020) ‘On collaborative filtering model optimised with multi-item attribute information space for enhanced recommendation accuracy’, Int. J. Intelligent Systems Technologies and Applications, Vol. 19, No. 3, pp.207–215.