Large Scale Semantic Trajectory Analysis and Applications

Abstract

The rapid growth of mobile devices and location-based services has led to an explosion of semantic trajectory data, creating significant challenges in efficient analysis and practical applications. Traditional methods often face limitations in computation, scalability, and privacy, especially when handling large datasets of semantic trajectories. This dissertation addresses these issues with three major contributions. First, it introduces AnotherMe, a distributed algorithm that uses Sequence-Sensitive Hashing (SSH) for efficient semantic grouping and similarity calculation, achieving significant improvements in processing speed and scalability. Second, it proposes GRU-KSS, a two-branch deep learning model that delivers real-time, accurate social community recommendations by analyzing semantic trajectories. Third, it develops a privacy preserving framework that utilizes semantic trajectories for travel buddy recommendations, combining a multi-server design with a privacy-preserving index tree to protect user data. Comprehensive experiments confirm the effectiveness of these methods, demonstrating superior scalability, accuracy, and privacy compared to existing approaches. These contributions highlight the potential of semantic trajectory analysis to drive advancements in intelligent, privacy-focused recommendation systems.