DPGR: Dual-Domain Spatiotemporal Generative Retrieval for Intent-Aware Local Life Service Recommendation

Abstract

Local life service recommendation (LLSR) spans content recommendation and Point-of-Interest (POI) recommendation. On platforms such as Dianping, users browse content (notes, videos, reviews) and interact with POIs (collecting restaurants, planning check-ins) within the same session. The core challenge is that dual domain actions are causally linked: a content click on a food review and a subsequent POI collect are two reflections of the same latent user intent, not merely two separate problems. Yet existing generative retrieval methods treat content and POI signals as independent, missing this shared latent structure. To address these limitations, we propose DPGR (Dual-domain Spatiotemporal Generative Retrieval), a unified dual-domain generative retrieval framework for LLSR. DPGR introduces a Spatiotemporal State-Conditioned Token Modeling mechanism that injects dynamic user context into multiple stages of the encoder, enabling state-dependent reweighting and adaptive preference balancing. Instead of directly generating items, DPGR learns discrete intent codes via quantization of dual-domain behaviors and predicts the top-K intents for the target session. Each intent code independently retrieves candidates via parallel ANN, achieving diverse coverage with no additional latency. Offline experiments on public and internal datasets show that DPGR outperforms state-of-the-art generative retrieval baselines. Online A/B tests on Dianping demonstrate significant gains in both domains: in the content domain, visit views increase by 1.486% and watch time by 1.015%; in the POI domain, POI clicks increase by 0.527% and POI collects by 5.209% (all p < 0.05).