Abstract
Recommendation system facilitates users promptly obtaining the information they need in this age of data explosion. Research on recommendation models have recognized the importance of integrating user historical behavior sequence into the model to alleviate the matrix sparsity. Although deep learning algorithm with attentive mechanism exhibits competitive performance in sequential recommendation, the searching for optimal attentive factors still lack effectiveness. In this work, we redesign the sequential recommendation model by employing swarm intelligence for optimization in the attentive mechanism thus to improve the algorithm accuracy. We conduct extensive comparative experiments to evaluate performance of four swarm intelligence algorithms and traditional recommendation methods. Our work is the first attempt to integrate swarm intelligence into sequential recommendation algorithm. Experimental results confirmed the superior performance on AUC score of the proposed approach.
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Acknowledgement
This study is supported by National Natural Science Foundation of China (71901150, 71901143), Natural Science Foundation of Guangdong (2022A1515012077), Guangdong Province Innovation Team “Intelligent Management and Interdisciplinary Innovation” (2021WCXTD002), Shenzhen Higher Education Support Plan (20200826144104001).
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Geng, S., Liang, G., He, Y., Duan, L., Xie, H., Song, X. (2022). Swarm Enhanced Attentive Mechanism for Sequential Recommendation. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_37
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DOI: https://doi.org/10.1007/978-3-031-09677-8_37
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