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DSIM: dynamic and static interest mining for sequential recommendation

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Abstract

Sequential recommendation aims to predict the next interaction by mining users’ evolving interest from their historical behaviors. Through comprehensive study, we argue that the evolving interest consists of dynamic interest and static interest, both of which are important for recommendation tasks. However, it is still a challenging task to model the dynamic interest accurately since it may change rapidly. Besides, existing approaches cannot fully exploit users’ static interest. In this paper, we propose a novel dynamic and static interest mining approach called DSIM for sequential recommendation. Specifically, DSIM takes advantage of the time information and learn the dynamic interest precisely with a time-aware neural Hawkes process. Furthermore, a side information-aware self-attention mechanism is designed to leverage side information in a non-invasive way to learn static interest. Finally, DSIM fuses the learned dynamic and static interest adaptively with the gating mechanism and generates the hybrid interest for sequential recommendation. Extensive experiments on three real-world datasets demonstrate that DSIM can effectively mine and model users’ interest, and it outperforms the state-of-the-art baselines in sequential recommendation tasks.

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Notes

  1. https://www.epicgames.com/fortnite.

  2. https://github.com/HduDBSI/DSIM.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Dongjin Yu, Jianjiang Chen & Dongjing Wang

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710126, China

    Yueshen Xu

  3. School of Computer and Computing Science, Zhejiang University City College, Hangzhou, China

    Zhengzhe Xiang

  4. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Shuiguang Deng

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  1. Dongjin Yu
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  2. Jianjiang Chen
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  3. Dongjing Wang
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  4. Yueshen Xu
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  5. Zhengzhe Xiang
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  6. Shuiguang Deng
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Correspondence to Dongjing Wang.

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This research was partially supported by the National Key Research and Development Program of China under No. 2019YFD1101105, Zhejiang Provincial Natural Science Foundation of China under No. LQ20F020015, the National Natural Science Foundation of China under No. 62102350 and Zhejiang Provincial Key Science and Technology Program Foundation under No. 2020C01165.

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Yu, D., Chen, J., Wang, D. et al. DSIM: dynamic and static interest mining for sequential recommendation. Knowl Inf Syst 64, 2267–2288 (2022). https://doi.org/10.1007/s10115-022-01715-3

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