  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.
