POI Alias Discovery in Delivery Addresses using User Locations

Abstract

People often refer to a place of interest (POI) by an alias. In ecommerce 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.

FastAddr

“…As important components in spatio-temporal data, addresses and POIs have received lots of research interests [9,16,35,39,41,44,51]. One of the most popular research areas is next POI recommendation [9,51].…”

“…Ruan et al [35] proposed a novel method to infer delivery time in logistics. He et al [16] designed a location-based method to discover POI alias. To obtain structured elements in addresses, some studies focus on the address segmentation problem [27,47].…”

“…Admittedly, some works have studied problems related to abnormal address detection. Instead of directly addressing the abnormal address issue, some studies focus on applications related to address and Point-of-Interest (POI), such as coordinates generating for POIs and addresses [39,41], and POI alias discovery [16]. Meanwhile, there are many anomaly detection works in different modalities, e.g., image [15], time series [4,10], graph data [52], and text data [26,32], which have applications such as pickpocket suspects detection [14] and malicious behavior detection on social media [34,50].…”