Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. There have been a variety of studies that have begun to explore its spatiotemporal properties, but a comprehensive and in-depth analysis of the OFRS spatiotemporal features is yet to be conducted. Therefore, this paper studies the OFRS based on three questions: how spatiotemporal features play a role; why self-attention cannot be used to model the spatiotemporal sequences of OFRS; and how to combine spatiotemporal features to improve the efficiency of OFRS. Firstly, through experimental analysis, we systemically extracted the spatiotemporal features of OFRS, identified the most valuable features and designed an effective combination method. Secondly, we conducted a detailed analysis of the spatiotemporal sequences, which revealed the shortcomings of self-attention in OFRS, and proposed a more optimized spatiotemporal sequence method for replacing self-attention. In addition, we also designed a Dynamic Context Adaptation Model to further improve the efficiency and performance of OFRS. Through the offline experiments on two large datasets and online experiments for a week, the feasibility and superiority of our model were proven.

翻译：在线食品推荐服务（OFRS）具有显著的时空特征以及能够及时便捷满足用户需求的优势。已有多种研究开始探索其时空属性，但尚未开展对OFRS时空特征全面而深入的分析。因此，本文基于三个问题对OFRS进行研究：时空特征如何发挥作用；为何不能使用自注意力机制对OFRS的时空序列进行建模；以及如何结合时空特征以提升OFRS的效率。首先，通过实验分析，我们系统地提取了OFRS的时空特征，识别了最具价值的特征，并设计了一种有效的组合方法。其次，我们对时空序列进行了详细分析，揭示了自注意力在OFRS中的不足，并提出了一种更优化的时空序列方法来替代自注意力。此外，我们还设计了一种动态上下文适应模型（Dynamic Context Adaptation Model），以进一步提升OFRS的效率和性能。通过在两个大型数据集上的离线实验和为期一周的在线实验，证明了我们模型的可行性和优越性。