Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918066140479488 |
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| author | Gong, Zhen Fan, Zhifang Lu, Hui Chen, Qiwei Zhang, Chenbin Guan, Lin Zheng, Yuchao Zhang, Feng Yang, Xiao Liu, Zuotao |
| author_facet | Gong, Zhen Fan, Zhifang Lu, Hui Chen, Qiwei Zhang, Chenbin Guan, Lin Zheng, Yuchao Zhang, Feng Yang, Xiao Liu, Zuotao |
| contents | Sequential recommendation, a critical task in recommendation systems, predicts the next user action based on the understanding of the user's historical behaviors. Conventional studies mainly focus on cross-behavior modeling with self-attention based methods while neglecting comprehensive user interest modeling for more dimensions. In this study, we propose a novel sequential recommendation model, Pyramid Mixer, which leverages the MLP-Mixer architecture to achieve efficient and complete modeling of user interests. Our method learns comprehensive user interests via cross-behavior and cross-feature user sequence modeling. The mixer layers are stacked in a pyramid way for cross-period user temporal interest learning. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a +0.106% improvement in user stay duration and a +0.0113% increase in user active days in the online A/B test. The Pyramid Mixer has been successfully deployed on the industrial platform, demonstrating its scalability and impact in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation Gong, Zhen Fan, Zhifang Lu, Hui Chen, Qiwei Zhang, Chenbin Guan, Lin Zheng, Yuchao Zhang, Feng Yang, Xiao Liu, Zuotao Information Retrieval Sequential recommendation, a critical task in recommendation systems, predicts the next user action based on the understanding of the user's historical behaviors. Conventional studies mainly focus on cross-behavior modeling with self-attention based methods while neglecting comprehensive user interest modeling for more dimensions. In this study, we propose a novel sequential recommendation model, Pyramid Mixer, which leverages the MLP-Mixer architecture to achieve efficient and complete modeling of user interests. Our method learns comprehensive user interests via cross-behavior and cross-feature user sequence modeling. The mixer layers are stacked in a pyramid way for cross-period user temporal interest learning. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a +0.106% improvement in user stay duration and a +0.0113% increase in user active days in the online A/B test. The Pyramid Mixer has been successfully deployed on the industrial platform, demonstrating its scalability and impact in real-world applications. |
| title | Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2506.16942 |