Personalized Route Recommendation Based on User Habits for Vehicle Navigation
<|reference_start|>Personalized Route Recommendation Based on User Habits for Vehicle Navigation: Navigation route recommendation is one of the important functions of intelligent transportation. However, users frequently deviate from recommended routes for various reasons, with personalization being a key problem in the field of research. This paper introduces a personalized route recommendation method based on user historical navigation data. First, we formulate route sorting as a pointwise problem based on a large set of pertinent features. Second, we construct route features and user profiles to establish a comprehensive feature dataset. Furthermore, we propose a Deep-Cross-Recurrent (DCR) learning model aimed at learning route sorting scores and offering customized route recommendations. This approach effectively captures recommended navigation routes and user preferences by integrating DCN-v2 and LSTM. In offline evaluations, our method compared with the minimum ETA (estimated time of arrival), LightGBM, and DCN-v2 indicated 8.72%, 2.19%, and 0.9% reduction in the mean inconsistency rate respectively, demonstrating significant improvements in recommendation accuracy.<|reference_end|>
@article{huang2024personalized,
    title={Personalized Route Recommendation Based on User Habits for Vehicle
  Navigation},
    author={Yinuo Huang, Xin Jin, Miao Fan, Xunwei Yang, Fangliang Jiang},
    journal={arXiv preprint arXiv:2409.14047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14047},
    primaryClass={cs.RO}
}