AMoS: Autonomous Multimodal POI Standardization without Extra Annotation

Abstract

Providing persuasive descriptions of points of interest (POI) is crucial for ensuring the quality of location-based services such as food delivery. However, informal textual descriptions by customers and unreliable geographic coordinates from indoor devices make it challenging to standardize both textual and geospatial descriptions of queried POIs. Previous works often take a retrieve-then-rank approach, which has limited feasibility in real-world delivery scenarios due to their dependency on a vast POI database and the extensive labor required for labeling. In this work, we propose the AMoS system, which is based on the observation that records referring to the same POI are either similar in both semantic and geospatial domains, or at least in one of them. The AMoS system leverages inherent semantic and geospatial similarities within historical POI records, combining them in graph-based clustering to retrieve candidates for standardizing a given query. We also propose a standardization paradigm that combines structured formatting rules of POI descriptions with content diversity. We evaluate the performance on 22356 orders from a real-world online delivery platform. Our retrieval performance outperforms baselines by 16% in precision and remains competitive compared to a supervised approach. Our standardization accuracy reaches 90.24%.