Title: A hybrid collaborative filtering recommendation algorithm: integrating content information and matrix factorisation

Authors: Jing Wang; Arun Kumar Sangaiah; Wei Liu

Addresses: School of Artificial Intelligence, Open University of Guangdong, Guangzhou, Guangdong, China ' School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India ' School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China

Abstract: Matrix factorisation is a one of the most popular techniques in recommendation systems. However, matrix factorisation still suffers from cold start problem and needs complicated computation. In this paper, we present a hybrid recommendation algorithm, which integrates user and item content information and matrix factorisation. First, based on user or item content information, biases of user or item can be evaluated in advance. Incorporating user and item biases into matrix factorisation model, we can obtain final prediction model. At last, momentum stochastic gradient descent method is used to optimise other parameters. Experimental results on a real data set have shown best performance of our algorithm in terms of MAE and RMSE when compared with other classical matrix factorisation recommendation algorithms.

Keywords: recommender system; collaborative filtering; matrix factorisation; momentum stochastic gradient descent.

DOI: 10.1504/IJGUC.2020.107616

International Journal of Grid and Utility Computing, 2020 Vol.11 No.3, pp.367 - 377

Received: 11 Jan 2019
Accepted: 15 May 2019
Published online: 02 Jun 2020 *

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