Title: Particle swarm optimisation-based contextual recommender systems

Authors: Mohammed Wasid; Rashid Ali; Vibhor Kant

Addresses: Department of Computer Engineering, Aligarh Muslim University, Aligarh-202002, India ' Department of Computer Engineering, Aligarh Muslim University, Aligarh-202002, India ' Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur-302031, India

Abstract: Collaborative filtering (CF) has been investigated and improved extensively over the past years but still unable to handle multiple issues like cold-start and sparsity problems due to the absence of user-item rating information. Further, it has been seen that the contextual information plays a significant role for generating user relevant situational recommendations but the incorporation of contextual information into CF directly is the major problem in RS. This paper is an effort toward developing recommendation strategy based on contextual fuzzy CF by utilising particle swarm optimisation (PSO) algorithm. This work has been completed in two-fold. First, we incorporate contextual information into fuzzy CF algorithm through context modelling approach. Second, we extend the previous method by employing PSO algorithm in order to learn user weights on various hybrid fuzzy features for enhancing the performance of CF technique. The results show the superiority of our proposed method against other comparative methods.

Keywords: collaborative filtering; CF; context-awareness; cold start; particle swarm optimisation; PSO; sparsity; recommender systems; RS.

DOI: 10.1504/IJSI.2017.087874

International Journal of Swarm Intelligence, 2017 Vol.3 No.2/3, pp.170 - 191

Available online: 30 Oct 2017 *

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