Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience. However, the relevant research is mainly based on driving trajectory of vehicles to predict the destinations, which is challenging to achieve the early destination prediction. To this end, we propose a model of early destination prediction, DP-BPR, to predict the destinations by users’ travel time and locations. There are three challenges to accomplish the model: 1) the extremely sparse historical data make it challenge to predict destinations directly from raw historical data; 2) the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction; 3) how to learn destination preferences from historical data. To deal with these challenges, we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks. We learn the embeddings not only for users but also for locations and time under the supervision of historical data, and then use Bayesian personalized ranking (BPR) to learn to rank destinations. Experimental results on the Zebra dataset show the effectiveness of DP-BPR.

目的地预测吸引了广泛的关注，因为它可以帮助车辆辅助系统提前推荐相关服务，以改善用户驾驶体验。然而，相关研究主要基于车辆的行驶轨迹来预测目的地，这对于实现早期目的地预测是具有挑战性的。为此，我们提出了一种早期目的地预测模型DP-BPR，以根据用户的旅行时间和位置来预测目的地。完成该模型面临三个挑战：1）极其稀疏的历史数据使直接从原始历史数据预测目的地成为挑战。2）目的地不仅与出发点有关，而且与出发时间有关，因此在预测时应将它们都考虑在内；3）如何从历史数据中学习目的地偏好。为了应对这些挑战，我们通过使用深度神经网络进行嵌入学习，将稀疏的高维数据映射到稠密的低维空间。我们不仅在历史数据的监督下为用户学习嵌入，而且为位置和时间学习嵌入，然后使用贝叶斯个性化排名（BPR）来学习对目的地进行排名。Zebra数据集上的实验结果表明DP-BPR的有效性。