摘要： 如何建模和学习用户行为的时间模式是时序推荐中的重要问题，但现有的时序推荐研究大多聚焦于单一类型行为的模式学习，未能充分挖掘多类型行为（如点击、购买、收藏等）的内在多元模式，制约了推荐效果的提升。本文着眼于行为数据中个体行为的多季节序列依赖特性以及不同类型行为间随时间变化的复杂依赖关系，构建了一类针对多类型行为时序模式学习的深度模型（MSMB）。具体而言，该模型包含一个融合了多尺度指数移动平均（EMA）机制的双通道序列编码器，能够精准捕捉各行为序列中多季节的时变规律；同时引入了跨行为依赖模块，兼顾不同周期粒度，以有效建模不同行为间的动态关联性。在三个基准数据集上进行的大量实验验证了所提模型的有效性和优越性。

Abstract: How to model and learn user’s behavior patterns is a crucial issue in temporal recommendation. However, the majority of existing research primarily centers on pattern learning within a single type of behavior. This limitation restricts the ability to take full advantage of the user's diversified behavior patterns revealed by various types of behaviors, such as clicking, purchasing, marking as favorite, and so on. As a result, the potential for enhancing recommendation performance remains underexplored. To address this gap, this research delves into the multi-seasonal sequential dependencies of individual behaviors and the intricate dependencies among different types of behaviors over time. Specifically, we propose a novel model, named multi-seasonal multi-behavior (MSMB) model, for learning temporal patterns across multiple behaviors. In the proposed model, a dual-channel sequence encoder is employed, which incorporates a multi-scale exponential moving average (EMA) mechanism to effectively capture the multi-seasonal temporal dependencies within individual behavior sequences. Additionally, a cross-behavior dependency module is introduced to account for different periodic granularities, thereby enabling the model to effectively capture the time-variant dependencies across various types of behaviors. Extensive experiments conducted on three benchmark datasets demonstrate the effectiveness and superiority of the proposed MSMB model in enhancing temporal recommendation performance.