Mining user similarity based on location history

TLDR

The pervasiveness of location‑acquisition technologies enables people to log spatio‑temporal data, and the growing availability of such data offers opportunities and challenges for automatically discovering knowledge from trajectories. The paper proposes the hierarchical‑graph‑based similarity measurement (HGSM) framework to mine geographic similarity between users from their location histories. HGSM models users’ trajectories as hierarchical graphs, incorporating movement sequence and geographic hierarchy, and is evaluated on six months of GPS data from 65 volunteers. The resulting user similarity improves information retrieval relevance for individuals, communities, and businesses, and HGSM outperforms cosine and Pearson similarity measures.

Abstract

The pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enable people to conveniently log the location histories they visited with spatio-temporal data. The increasing availability of large amounts of spatio-temporal data pertaining to an individual's trajectories has given rise to a variety of geographic information systems, and also brings us opportunities and challenges to automatically discover valuable knowledge from these trajectories. In this paper, we move towards this direction and aim to geographically mine the similarity between users based on their location histories. Such user similarity is significant to individuals, communities and businesses by helping them effectively retrieve the information with high relevance. A framework, referred to as hierarchical-graph-based similarity measurement (HGSM), is proposed for geographic information systems to consistently model each individual's location history and effectively measure the similarity among users. In this framework, we take into account both the sequence property of people's movement behaviors and the hierarchy property of geographic spaces. We evaluate this framework using the GPS data collected by 65 volunteers over a period of 6 months in the real world. As a result, HGSM outperforms related similarity measures, such as the cosine similarity and Pearson similarity measures.