Title: Collaborative variational factorisation machine for recommender system

Authors: Jiwei Qin; Honglin Dai

Addresses: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China ' College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China

Abstract: At present, the recommendation system is confronting the huge challenge of data sparsity and high complexity of algorithm. Like the traditional collaborative filtering recommendation methods, they are difficult to adapt to the data sparse environment, resulting in low prediction accuracy. To address the issues, this paper presents a novel factorisation machine based on Collaborative filtering framework called collaborative variational factorisation machine (CVFM) that considers the user-user relation with the interaction data for Recommender systems. First, the user-item explicit ratings are used to build the user-user relationship by the similarity calculation. Next, we develop a variational factorisation machine to exploit the inherent relationship of latent variables from interaction information. The experimental results on three different sparsity datasets show that the presented CVFM is superior to other popular method in prediction accuracy, at the same time, maintain the stability of our algorithm with dealing with sparse data.

Keywords: recommendation system; factorisation machine; collaborative filtering; prediction model; a novel factorisation machine; CVFM; collaborative variational factorisation machine.

DOI: 10.1504/IJAACS.2023.131069

International Journal of Autonomous and Adaptive Communications Systems, 2023 Vol.16 No.2, pp.175 - 187

Received: 19 Jan 2020
Accepted: 31 Aug 2020
Published online: 24 May 2023 *

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