Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Abstract

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads. But how to alleviate sparsity and further enhance accuracy is still challenging. Addressing at these issues, we propose to fuse spatio-temporal contexts into deep factorization machine (STC_DeepFM) offline for pick-up area recommendation, and within the area to recommend pick-up points online using factorization machine (FM). Firstly, we divide the urban area into several grids with equal size. Spatio-temporal contexts are destilled from pick-up points or points-of-interest (POIs) belonged to the preceding grids. Secondly, the contexts are integrated into deep factorization machine (DeepFM) to mine high-order interaction relationships from grids. And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation. Thirdly, we devise the architecture of offline-to-online (O2O) recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency. Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts, different recommendation models, and the O2O architecture. The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods, and the O2O architecture achieves excellent real-time performance.

Automatic Depression Detection Among Higher Education Students Based on DeepFM

“…With the behavioral data containing both discrete and continuous characteristics, the DeepFM model can automatically learn depression detection patterns among students. DeepFM is a deep learning structure for discrete-continuous mixed data as input, which has been widely used in multiple tasks, such as metal-organic properties prediction [24], passenger car sales prediction prediction [25], taxi pick-up area recommendation [26], and etc.…”