Authors: Chonghuan Xu; Jie Wang; Jiangjun Yuan
Addresses: Business Administration College, Zhejiang Gongshang University, Hangzhou City, China ' Business Administration College, Zhejiang Gongshang University, Hangzhou City, China; School of Shangmao, Zhejiang Institute of Economics and Trade College, Hangzhou City, 310018, China ' School of Shangmaolvyou, Hangzhou Vocational and Technical College, Hangzhou, 310018, China
Abstract: Recommender systems are widely used to provide e-commerce users appropriate items and have emerged in response to the problem of information overload. Collaborative filtering (CF) is one of the most successful recommender methods which recommend items to a given user based on the opinions of the similar users. However, the existing CF methods lack the consideration of factors such as time and geo-location. In this paper, we take into account many influencing factors including time and geo-location in the process of similarity computation. The simulation results on two real-world data sets show that our algorithm achieves superior performance to existing methods.
Keywords: recommender systems; collaborative filtering; CF; multi-factors.
International Journal of Computing Science and Mathematics, 2020 Vol.11 No.1, pp.29 - 39
Received: 28 Jul 2018
Accepted: 04 Sep 2018
Published online: 27 Feb 2020 *