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An improved collaborative filtering model based on time weighted correlation coefficient and inter-cluster separation

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Abstract

The recommendation system is a good choice to provide users with personalized services. Facing with a large amount of data, clustering technology can group similar users into one category, which can make more rapid and accurate recommendations for target users. Due to user’s interests changing over time, how to make reasonable recommendations is an important issue. In this article, an improved k-means clustering algorithm based on cuckoo search is proposed and an improved time correlation coefficient is added to the algorithm to improve the accuracy of recommendation system. The proposed clustering algorithm integrates intra-cluster compactness and inter-cluster separation, which can improve the similarity of users in the same cluster. The proposed time correlation coefficient creatively considers both the inherent connection between user’s preference and time, and the impact of the periodicity and continuity of time on user rating patterns.

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Acknowledgements

This research is jointly supported by Photovoltaic Industry Production Production Integration Comprehensive Demonstration Base of Sichuan Province (Sichuan Financial Education [2022] No.106)，the project of Sichuan Provincial Department of Education (No. JG2021-550), and the key project of Southwest Petroleum University (No.X2021JGZDI033). The numerical calculations in this paper have been done on the super-computing system in the Supercomputing Center for science and engineering of Southwest Petroleum University.

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  1. Donghong Tian, Qianqian Wu and Min Li have contributed equally to this work.

Authors and Affiliations

  1. School of Sciences, Southwest Petroleum University, Chengdu, 610500, Sichuan, China

    Ruike Lan, Donghong Tian, Qianqian Wu & Min Li

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  1. Ruike Lan
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  2. Donghong Tian
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  3. Qianqian Wu
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Correspondence to Donghong Tian.

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Lan, R., Tian, D., Wu, Q. et al. An improved collaborative filtering model based on time weighted correlation coefficient and inter-cluster separation. Int. J. Mach. Learn. & Cyber. 14, 3543–3560 (2023). https://doi.org/10.1007/s13042-023-01849-y

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