Swarm Enhanced Attentive Mechanism for Sequential Recommendation

Shuang GENG, Gemin LIANG, Yuqin HE, Liezhen DUAN, Haoran XIE*, Xi SONG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review


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.
Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence
Subtitle of host publication13th International Conference, ICSI 2022, Xi'an, China, July 15–19, 2022, Proceedings, Part I
EditorsYing TAN, Yuhui SHI, Ben NIU
PublisherSpringer, Cham
Number of pages12
ISBN (Electronic)9783031096778
ISBN (Print)9783031096761
Publication statusPublished - Jun 2022
Event13th International Conference on Sensing and Imaging - Xi'an, China
Duration: 15 Jul 202219 Jul 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Sensing and Imaging
Abbreviated titleICSI 2022

Bibliographical note

Funding 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).

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.


  • Sequential recommendation
  • Swarm intelligence
  • Attentive mechanism


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