Hierarchical temporal–spatial preference modeling for user consumption location prediction in Geo-Social Networks

Abstract

Predicting where people will consume in the future is of great significance for promoting local business. Although the prevalence of Geo-Social Networks (GSNs) has provided sufficient and desirable geo-tagged data for user mobility modeling, most studies attempt to directly fit user’s preference toward locations through exploring the complex interaction between user,location pairs, which is usually hard to incorporate temporal–spatial context and side information. Moreover, the availability of multi-modal data associated with both user and location in GSNs has not yet been comprehensively leveraged. In view of the above-mentioned situations, in this article, we propose a two-stage framework composed of a Temporal Base Model (TBM) and a Location Prediction Model (LPM) to accomplish the task of user consumption location prediction at a given time in the future. In the first stage, based on user sentimental textual reviews, we leverage the hierarchical attention mechanism to capture time-sensitive user latent preference. In the second stage, we fuse the multifaceted context to derive the user’s consumption probability toward different locations at the given time. We conduct extensive experiments over three real-world GSN datasets to verify the performance of the proposed approach. The experimental results encouragingly demonstrate the effectiveness of the two-stage framework, which outperforms multiple baselines in terms of different evaluation metrics such as accuracy, average percentile rank (APR) and coverage ratio.