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arXiv · 2506.16942

Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation

Zhen Gong·Zhifang Fan·Hui Lu·Qiwei Chen·Chenbin Zhang·Lin Guan·Yuchao Zheng·Feng Zhang·Xiao Yang·Zuotao Liu
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Abstract

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.

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Zhen Gong, Zhifang Fan, Hui Lu, Qiwei Chen, Chenbin Zhang, Lin Guan, Yuchao Zheng, Feng Zhang, Xiao Yang, Zuotao Liu. 2025-06-20. Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation. https://doi.org/10.1145/3726302.3731968

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