POI Alias Discovery in Delivery Addresses using User Locations

TL;DR

This paper presents a framework for discovering POI aliases in delivery addresses by analyzing user mobility profiles, improving address accuracy in logistics systems without heavy manual labeling.

Contribution

It introduces a novel alias discovery method leveraging user location data and mobility profile similarity, reducing reliance on manual labeling.

Findings

Effective alias detection on large-scale data

Improved delivery address accuracy

Validated with JD logistics data

Abstract

People often refer to a place of interest (POI) by an alias. In e-commerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.