为序列推荐设计了首个感知上下文的位置编码方法。
A Contextual-Aware Position Encoding for Sequential Recommendation
- 提出上下文感知位置编码，动态捕捉用户行为顺序
- 在多个数据集上提升主流模型性能，大小模型均有效
- 已在真实商业平台部署，适合工业级推荐系统研究
序列推荐（SR）通过编码用户行为序列来预测下一步动作，已成为个性化推荐系统的主流策略。现代SR模型依赖注意力机制融合历史行为，但该机制通常对顺序不敏感，依赖位置编码（PE）。现有方法仅给每个位置分配可学习向量，提升有限。针对序列推荐的特殊需求，我们提出首个专为此任务设计的位置编码方法——上下文感知位置编码（CAPE）。在多个基准数据集上的实验表明，CAPE能持续提升多种主流模型性能，无论模型规模大小。此外，我们在真实商业平台上部署了该方法，验证其有效性。代码已开源。
原文摘要 · Abstract (English)
Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. A critical component of modern SR models is the attention mechanism, which synthesizes users' historical activities. This mechanism is typically order-invariant and generally relies on position encoding (PE). Conventional SR models simply assign a learnable vector to each position, resulting in only modest gains compared to traditional recommendation models. Moreover, limited research has been conducted on position encoding tailored for sequential recommendation, leaving a significant gap in addressing its unique requirements. To bridge this gap, we propose a novel Contextual-Aware Position Encoding method for sequential recommendation, abbreviated as CAPE. To the best of our knowledge, CAPE is the first PE method specifically designed for sequential recommendation. Comprehensive experiments conducted on benchmark SR datasets demonstrate that CAPE consistently enhances multiple mainstream backbone models and achieves state-of-the-art performance, across small and large scale model size. Furthermore, we deployed CAPE in an industrial setting on a real-world commercial platform, clearly showcasing the effectiveness of our approach. Our source code is available at https://github.com/yjdy/CAPE.
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