A Momentum Contrastive Learning Framework for Query-POI Matching
Yuting Qiang, Jianbing Zheng, Lixia Wu, Haomin Wen, Junhong Lou, Minhui Deng · 2024
The query-POI matching task involves interpreting noisy textual addresses to retrieve corresponding Points-of-Interest (POIs), which is crucial for location-based service providers. However, existing methods typically rely on annotated user search logs, limiting their generalization. This paper address the query-POI matching problem through geographical data alignment using a contrastive learning framework. Our model, MoCo-GA (Momentum Contrastive Geographical Alignment), learns similar representations for various geographical data elements of the same POI, including query address, POI address, and geolocation. We developed a method to create a cross-modal geographical dataset from crowd-sourced data for training. Our MoCo-GA employs the momentum contrastive instance discrimination mechanism to learn representations for textual addresses. We further proposed a siamese contrastive learning module to to for geographical data alignment. Experimental results demonstrate that MoCo-GA can consistently outperform baseline methods on query-POI matching task, particularly in zero-shot scenarios. Our code is available at https://github.com/CainiaoTechAi/TextGeoAlign