To read this content please select one of the options below:

UICF: a new user-item composite filtering recommendation framework by leveraging temporal semantics

Qingting Wei (School of Software, Nanchang University, Nanchang, China)
Xing Liu ( School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China) (Nanchang Kindly (KDL) Medical Technology Co., Ltd., Nanchang, China)
Daming Xian ( School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China) (Nanchang Kindly (KDL) Medical Technology Co., Ltd., Nanchang, China)
Jianfeng Xu (School of Software, Nanchang University, Nanchang, China) (Nanchang Kindly (KDL) Medical Technology Co., Ltd., Nanchang, China)
Lan Liu ( School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China) (Nanchang Kindly (KDL) Medical Technology Co., Ltd., Nanchang, China)
Shiyang Long (School of Software, Nanchang University, Nanchang, China)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 24 June 2024

Issue publication date: 17 July 2024

31

Abstract

Purpose

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

Design/methodology/approach

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Findings

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

Originality/value

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

Keywords

Acknowledgements

This work is supported by the National Natural Science Foundations of China (Grant No.62362050, Grant No.62266032), and Jiangxi Training Program for Academic and Technical Leaders in Major Disciplines - Leading Talents Project (Grant No.20225BCI22016).

§Qingting Wei and Xing Liu contributed equally to this work.

Citation

Wei, Q., Liu, X., Xian, D., Xu, J., Liu, L. and Long, S. (2024), "UICF: a new user-item composite filtering recommendation framework by leveraging temporal semantics", International Journal of Intelligent Computing and Cybernetics, Vol. 17 No. 3, pp. 577-604. https://doi.org/10.1108/IJICC-01-2024-0016

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

Related articles