  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.
