Taxi Pick-Up Area Recommendation via Integrating Spatio-Temporal Contexts into XDeepFM

Abstract

Taxi pick-up area recommendation based on GPS data can effectively improve efficiency and reduce fuel consumption. Most of the methods use the long-term GPS data, which makes recommendation accuracy low. Therefore, we propose a novel approach of integrating spatio-temporal contexts into the extreme Deep Factorization Machine (xDeepFM) for taxi pick-up area recommendation. In the training process, the urban area is divided into several grids of equal size, we extract pick-up points from the original GPS data. The pick-up points and points-of-interest (POIs) are mapped into the corresponding grids, we distil the spatio-temporal features from these grids to construct spatio-temporal contexts matrix. Then, the spatio-temporal contexts matrix is input into xDeepFM for training, and we get the taxi pick-up area recommendation model. xDeepFM not only can make feature interactions occur at the vector-wise in both implicit and explicit ways, but also learn both low-order and high-order feature interactions. xDeepFM can effectively enhance recommendation accuracy. Finally, the recommendation model is embedded in the system for testing. Evaluate on the public dataset of DiDi, we compare different recommendation methods. The experimental results show that our approach can effectively cope with the data sparseness problem, obtain excellent performance, and is superior to some state-of-the-art methods. The RMSE is only 0.8%, MAE is about 7%, and the explained variance score is over 98%.