POI Alias Discovery in Delivery Addresses using User Locations
This addresses the POI alias issue in e-commerce logistics to improve delivery accuracy and reduce manual labeling costs, representing an incremental domain-specific advancement.
The paper tackles the problem of discovering aliases for places of interest (POIs) in delivery addresses by leveraging user GPS location data, proposing a framework that models mobility profile similarity and achieves validated effectiveness on large-scale logistics data.
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.