TY - EJOU AU - Liu, Yizhi AU - Qing, Rutian AU - Zhao, Yijiang AU - Wang, Xuesong AU - Liao, Zhuhua AU - Li, Qinghua AU - Cao, Buqing TI - Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation T2 - Computer Systems Science and Engineering PY - 2023 VL - 45 IS - 3 SN - AB - 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. KW - Location-based service (LBS); trajectory data mining; offline-to-online (O2O) recommendation; deep factorization machine (DeepFM); spatio-temporal context DO - 10.32604/csse.2023.021615