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.
|Title of host publication||Advances in Swarm Intelligence|
|Subtitle of host publication||13th International Conference, ICSI 2022, Xi'an, China, July 15–19, 2022, Proceedings, Part I|
|Editors||Ying TAN, Yuhui SHI, Ben NIU|
|Number of pages||12|
|Publication status||Published - Jun 2022|
|Event||13th International Conference on Sensing and Imaging - Xi'an, China|
Duration: 15 Jul 2022 → 19 Jul 2022
|Name||Lecture Notes in Computer Science|
|Conference||13th International Conference on Sensing and Imaging|
|Abbreviated title||ICSI 2022|
|Period||15/07/22 → 19/07/22|
Bibliographical noteFunding Information:
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).
© 2022, Springer Nature Switzerland AG.
- Sequential recommendation
- Swarm intelligence
- Attentive mechanism