User Identity Linkage across Location Based Social Networks with Spatio Temporal Check in Patterns
Driven by the tremendous amount of spatiotemporal data obtained from location based social networks, the implement of cross domain user linkage, also known as the User Identity Linkage (UIL) brings about abundant promising research and application prospects. User distinctive behavior patterns implicit in the "check in" spatio temporal data provide a utility and meritorious way for UIL research. Enlightened by the fundamental weakness of existing algorithms which discretize the spatio temporal sparse data with continuous nature, we propose a pertinently approach CP Link to complete UIL task by exploiting user behavior pattern in a continuous way, where the continuous space is divided into irregularly shaped stay regions and a continuous time based IDTW method is utilized to calculate the similarity. To bridge the gap between the theoretical ideal model and the actual sparse data, we apply user associated location frequent pattern (LFP) model to supply the sparse deficiency. Ultimately, extensive experiments on real world datasets are conducted to demonstrate the superiority and stability of our proposed CP Link, which outperforms state of the art by more than 20% in terms of an AUC increase.
2020 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom)