发表机构
Rajax Network Technology; Yale School of Public Health（睿佳网络科技; 耶鲁大学公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有推荐模型难以区分时间动态与局部顺序线索的问题，提出解耦时间编码框架，通过两个互补模块分离时间与顺序信息，实现高效的生成式推荐。
AI 中文摘要
位置编码是基于Transformer的生成式推荐模型的核心组件，该模型将用户历史建模为自回归物品序列。大多数位置编码方法继承自自然语言处理，主要表示离散的物品顺序。然而，推荐序列不止是有序列表，时间戳和时间效应也会影响物品关系。本研究的动机来自一个现实世界的外卖和即时零售推荐系统，其中用户行为呈现多层次的时间规律，包括近因效应、用餐时间高峰、工作日-周末变化以及促销驱动的流量爆发。现有方法通过时间戳特征、间隔嵌入、衰减函数或注意力偏差部分解决该问题，但通常通过统一表示或单一建模路径注入异构时间信号，难以区分广泛的时间动态与局部顺序线索。为解决此局限，我们提出解耦时间编码（Decoupled Temporal Encoding，DTE），一种用于生成式推荐的轻量级框架。DTE通过两个互补模块将时间动态与顺序信息分离：个性化宏观时间模块，将紧凑的时间基元注入物品嵌入；时间门控微序列模块，仅在交互时间密集时引入相对顺序偏差。DTE还具有参数高效性和部署友好性，可轻松集成到现有系统中。
英文摘要
Positional encoding is a fundamental component of Transformer-based generative recommendation models, where user histories are modeled as autoregressive item sequences. Most positional encoding methods are inherited from natural language processing and mainly represent discrete item order. However, recommendation sequences go beyond ordered lists, as timestamps and temporal effects also shape item relations. Our work is motivated by a real-world food delivery and instant retail recommendation system, where user behavior exhibits multi-level temporal regularities, including recency effects, meal-time peaks, weekday-weekend shifts, and promotion-driven traffic bursts. Existing methods partially address this issue through timestamp features, interval embeddings, decay functions, or attention biases, but they usually inject heterogeneous temporal signals through a unified representation or a single modeling pathway, making it difficult to distinguish broad temporal dynamics from local order cues. To address this limitation, we propose Decoupled Temporal Encoding (DTE), a lightweight framework for generative recommendation. DTE separates temporal dynamics from order information through two complementary modules: a personalized macro-temporal module that injects compact temporal primitives into item embeddings, and a time-gated micro-sequential module that introduces relative-order bias only when interactions are temporally dense. DTE is also parameter-efficient and deployment-friendly, allowing easy integration into existing systems.
Commentsaccepted by CIKM '26
