Open Access

ARTICLE

A Novel Collaborative Filtering Algorithm and Its Application for Recommendations in E-Commerce

Jie Zhang^1,5, Juan Yang^2,*, Li Wang³, Yizhang Jiang⁴, Pengjiang Qian⁴, Yuan Liu⁴
1 School of Design, Jiangnan University, Wuxi, 214122, China
2 Department of Clothing Design and Engineering, Nantong University, Nantong, 226001, China
3 Research Center for Intelligence Information Technology, Nantong University, Nantong, 226001, China
4 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China
5 Management and Science University, Shah Alam, 40100, Selangor, Malaysia
* Corresponding Author: Juan Yang. Email:
(This article belongs to this Special Issue: Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce)

Computer Modeling in Engineering & Sciences 2021, 126(3), 1275-1291. https://doi.org/10.32604/cmes.2021.012112

Received 03 June 2020; Accepted 12 August 2020; Issue published 19 February 2021

Abstract

With the rapid development of the Internet, the amount of data recorded on the Internet has increased dramatically. It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data. In this study, we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce. This study has two main innovations. First, we propose a mechanism that embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item. Second, we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization. We compared the proposed method with several state-of-the-art alternatives on real datasets. The experimental results show that our proposed method outperforms the prevailing approaches.

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Keywords

Collaborative filtering; temporal behavior; probability matrix factorization

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