Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation

TL;DR

This paper introduces SPCS, a novel recommendation method that models spatiotemporal periodicity and collaborative signals using a spatiotemporal graph transformer, significantly improving local-life service recommendations.

Contribution

The paper proposes a new spatiotemporal graph transformer layer that explicitly encodes relative contexts and unifies periodicity with collaborative signals for better recommendations.

Findings

Achieves state-of-the-art performance on public datasets.

Effectively models user preferences across locations and times.

Outperforms existing methods in accuracy and robustness.

Abstract

Online local-life service platforms provide services like nearby daily essentials and food delivery for hundreds of millions of users. Different from other types of recommender systems, local-life service recommendation has the following characteristics: (1) spatiotemporal periodicity, which means a user's preferences for items vary from different locations at different times. (2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times. However, most existing methods either focus on merely the spatiotemporal contexts in sequences, or model the user-item interactions without spatiotemporal contexts in graphs. To address this issue, we design a new method named SPCS in this paper. Specifically, we propose a novel spatiotemporal graph transformer (SGT) layer, which explicitly encodes relative spatiotemporal contexts, and aggregates the information from multi-hop neighbors to unify spatiotemporal periodicity and collaborative signal. With extensive experiments on both public and industrial datasets, this paper validates the state-of-the-art performance of SPCS.