Social Community Recommendation based on Large-scale Semantic Trajectory Analysis Using Deep Learning

Abstract

The widespread use of smart mobile devices has resulted in a massive accumulation of trajectory data by service providers. The analysis of human trajectories, particularly semantic location information, has opened up avenues for discovering common social behavior and enhancing social connections, leading to a range of applications such as friend recommendations and product suggestions. However, the exponential growth of trajectory information generated every day presents significant challenges for existing trajectory analysis algorithms, which are no longer capable of delivering timely analysis results. To address this issue, we propose a highly efficient algorithm that can recommend social communities for new users in real time by leveraging knowledge gained from large-scale semantic trajectories. Specifically, we develop a novel two-branch deep neural network model that extracts semantic meanings at different levels of granularity from human trajectories and uncovers the hidden relationship between trajectories and social communities. We then utilize this model to perform instant social community recommendations. Our experimental results have demonstrated that our approach is not only significantly faster than traditional trajectory analysis algorithms in terms of social community recommendation, but also preserves high prediction accuracy with F1-score above 97%.