CP-Link: Exploiting Continuous Spatio-Temporal Check-In Patterns for User Identity Linkage

Abstract

Driven by the large amount of spatio-temporal data obtained from location-based social networks, the implementation of cross-domain user linkage, also known as the User Identity Linkage (UIL), has attracted increasing research attentions. While most of the existing UIL works discretize the spatio-temporal sparse data when identifying encountering or co-located events for UIL, user's distinctive behavior patterns implicit in the ‘`check-in’' spatio-temporal data with continuous nature pave the way for enhancing UIL performance. In this paper, we propose an approach dubbed {\it CP-Link} that exploits user behavior patterns in a continuous way. In CP-Link, the continuous space is divided into irregularly shaped stay regions, and a continuous time-based improved dynamic time warping (IDTW) method is proposed to calculate the similarity. {\color{blue}To bridge the gap between the ideal scenario with ample records and the reality with sparse data}, we adopt the user-associated location frequent pattern (LFP) model to compensate for the sparse deficiency. Extensive experiments conducted on real-world datasets demonstrate the effectiveness and superiority of CP-Link, which outperforms the state of the arts by more than 20% in terms of the AUC.