Time-Dependent Enhanced Personalized Semantic Map-Based Transportation Mode Recommendation System

The abundance of online data has presented a challenge for users to efficiently navigate and access the information they seek. To address this issue, this study proposes a time-dependent enhanced personalized semantic map-based recommendation system aimed at enhancing the personalized user experience. The results demonstrate that the utilization of this system significantly improves the accuracy of personalized recommendations, showcasing up to a 65% enhancement in prediction for a single user and up to a 50% enhancement for two users at different times. These findings exemplify the effectiveness of the proposed recommendation system in enhancing user experience through dynamic and behavior-based personalization. By tailoring the user interface for individual users and analyzing their navigational patterns, the system provides more precise and relevant recommendations, thereby facilitating the easy retrieval of desired information. This study underscores the importance of personalization in today's digital landscape and highlights the potential for further advancements through the utilization of sophisticated recommendation systems.