Research on collaborative filtering recommendation algorithm based on comprehensive cloud model similarity

Xinyu Cui; Xuepin Guo; Ziqiang Luo

doi:10.1117/12.3051773

18 November 2024 Research on collaborative filtering recommendation algorithm based on comprehensive cloud model similarity

Xinyu Cui, Xuepin Guo, Ziqiang Luo

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030L (2024) https://doi.org/10.1117/12.3051773
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Collaborative filtering recommendation system is one of the core technologies for product recommendations on ecommerce platforms. The method used to measure user similarity directly impacts the accuracy of the recommendation algorithm. Addressing deficiencies in similarity calculation within collaborative filtering recommendation algorithms, this paper proposes a comprehensive similarity measurement method based on the cloud model's "mutual membership degree - shape". This method overcomes the limitation of strict matching of object attributes, effectively utilizes cloud model parameters and data statistics to more accurately reflect user preferences, thus providing more precise recommendations to users. Experimental results demonstrate that this approach has significantly improved recommendation accuracy to a certain extent.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyu Cui, Xuepin Guo, and Ziqiang Luo "Research on collaborative filtering recommendation algorithm based on comprehensive cloud model similarity", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030L (18 November 2024); https://doi.org/10.1117/12.3051773

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