基于用户习惯的导航推荐，提升路线匹配度。
Personalized Route Recommendation Based on User Habits for Vehicle Navigation
- 融合历史导航数据与DCN-v2+LSTM建模用户偏好
- 比最小预计到达时间方法低8.72%不一致率
- 适合个性化出行规划、智能导航系统优化
智能交通中的导航路线推荐是重要功能，但用户常偏离推荐路线，个性化是核心挑战。本文提出一种基于用户历史导航数据的个性化路线推荐方法。首先，将路线排序建模为点对点问题，利用大量相关特征；其次，构建路线特征与用户画像，建立综合特征数据集；进一步提出深度交叉循环（DCR）学习模型，通过结合DCN-v2与LSTM，有效捕捉推荐路线与用户偏好。离线评估显示，该方法相较最小预计到达时间（ETA）、LightGBM和DCN-v2，平均不一致率分别降低8.72%、2.19%和0.9%，显著提升推荐准确性。
原文摘要 · Abstract (English)
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.
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