Exploring the Spatiotemporal Features of Online Food Recommendation Service

TL;DR

This paper provides a comprehensive analysis of the spatiotemporal features in Online Food Recommendation Services, proposing optimized methods to enhance their efficiency and performance based on experimental validation.

Contribution

It systematically extracts and combines spatiotemporal features, identifies limitations of self-attention, and introduces a Dynamic Context Adaptation Model for improved OFRS performance.

Findings

Identified key spatiotemporal features for OFRS

Proposed a new sequence modeling method surpassing self-attention

Validated the model's effectiveness through large-scale experiments

Abstract

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.