用金字塔结构建模用户多维度兴趣，提升推荐精准度。
Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation
- 采用MLP-Mixer架构，分层建模跨行为与跨特征兴趣
- 在线实验中用户停留时长提升0.106%，活跃天数增0.0113%
- 适合工业级推荐系统，支持高效部署与多周期兴趣捕捉
序列推荐是推荐系统中的关键任务，旨在基于用户历史行为预测其下一步动作。传统方法主要依赖自注意力机制进行跨行为建模，却忽视了多维度用户兴趣的全面刻画。本文提出一种新型序列推荐模型Pyramid Mixer，利用MLP-Mixer架构实现高效且完整的用户兴趣建模。该方法通过跨行为与跨特征的用户序列建模，学习全面的兴趣表示；同时以金字塔结构堆叠混洗层，实现跨周期的时间兴趣学习。大量离线与在线实验验证了方法的有效性与效率：在线A/B测试中，用户停留时长提升0.106%，活跃天数增加0.0113%。Pyramid Mixer已在工业平台成功部署，展现出良好的可扩展性与真实应用价值。
原文摘要 · Abstract (English)
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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