Spatial and semantical label inference for social media

摘要

Exploring the spatial and semantical knowledge from messages in social media offers us an opportunity to get a deeper understanding about the mobility and activity of users, which can be leveraged to improve the service quality of online applications like recommender systems. In this paper, we investigate the problem of the spatial and semantical label inference, where the challenges come from three aspects: diverse heterogeneous information, uncertainty of individual mobility, and large-scale sparse data. We address the challenges by exploring two types of data fusion, the fusion of heterogeneous social networks and the fusion of heterogeneous features. We build a 4-dimensional tensor, called spatial–temporal semantical tensor (STST), to model the individual mobility and activity by fusing two heterogeneous social networks, a social media network and a location-based social network (LBSN). To address the challenge arising from diverse heterogeneous information and the uncertainty of individual mobility, we construct three types of heterogeneous features and fuse them with STST by exploring their interdependency relationships. Particularly, a spatial tendency feature is constructed to constrain the inference of individual mobility and reduce the uncertainty. To deal with large-scale sparse data, we propose a parallel contextual tensor factorization (PCTF) to concurrently factorize STST. Finally, we integrate these components into an inference framework, called spatial and semantical label inference SSLI. The results of extensive experiments conducted on real datasets and synthetic datasets verify the effectiveness and efficiency of SSLI.