Electrical engineering

Physics

Intelligent transportation system

Recommender system

User agent

Human–computer interaction

Operating system

Optics

World Wide Web

Engineering

Information system

Focus (optics)

Transport engineering

User information

Session (web analytics)

User satisfaction

Computer science

User modeling

User interface

IEEE Transactions on Intelligent Transportation Systems

In the intelligent transportation system, the session data usually represents the users' demand. However, the traditional approaches only focus on the sequence information or the last item clicked by the user, which cannot fully represent user preferences. To address this issue, this paper proposes an Multi-aspect Aware Session-based Recommendation (MASR) model for intelligent transportation services, which comprehensively considers the user's personalized behavior from multiple aspects. In addition, it developed a concise and efficient transformer-style self-attention to analyze the sequence information of the current session, for accurately grasping the user's intention. Finally, the experimental results show that MASR is available to improve user satisfaction with more accurate and rapid recommendations, and reduce the number of user operations to decrease the safety risk during the transportation service.

Multi-Aspect Aware Session-Based Recommendation for Intelligent Transportation Services