基于稀疏轨迹数据的出租车载客区域推荐

基于短期出租车轨迹数据的载客区域推荐能极大减少系统开销，提高推荐效率，但常伴随着数据稀疏性的问题.针对该问题，本文提出了一种融合地理信息的隐语义模型-GeoLFM.该模型通过将出租车司机所处的客观地理环境信息，融合到司机-载客区域矩阵分解的过程中，从而弥补数据稀疏性带来的不足.同时，根据出租车实时的空间位置信息，为身处不同地点的出租车推荐不同的载客区域.实验证明，本文提出的方法与常用方法相比，推荐结果与真实的出租车司机载客情况间的平均绝对误差和均方根误差都得到大幅降低，较好的提升了推荐效果.

Taxi Pick-Up Area Recommendation Based on Sparse Trajectory Data

Taxi pick-up areas recommendation based on the short-term taxi trajectory data can greatly reduce the system overhead and improve the efficiency of the recommendation,but it often has the problems of data sparseness.For this reason,a Latent Factor Model integrated with the geographic information,called GeoLFM,is put forward.This model makes up the faultiness of data sparseness by integrating the geographic information relating to drivers into the Matrix decomposition,which records the visiting relationship between drivers and pick-up areas.Meanwhile,different pick-up areas can be recommended for the taxis in various locations according to the real-time spatial context of the taxis.Experimental results show that,with the comparison between our proposed method and others,the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) between the recommended results and the actual value are significantly reduced,which indicates the recommendation effect is better improved.