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

作　　者：Yizhi Liu Rutian Qing Yijiang Zhao Xuesong Wang Zhuhua Liao Qinghua Li Buqing Cao 

机构地区：[1]School of Computer Science and Engineering,Hunan University of Science and Technology,Xiangtan,411201,China [2]Northwestern Institute on Complex Systems,Northwestern University,Evanston,IL,60208,USA [3]Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province,Xiangtan,411201,China

出　　处：《Computer Systems Science & Engineering》2023年第6期2505-2519,共15页计算机系统科学与工程（英文）

基　　金：supported by the National Natural Science Foundation of China(41871320,61873316);the Key Project of Hunan Provincial Education Department(19A172);the Scientific Research Fund of Hunan Provincial Education Department(18K060);the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20211000).

摘　　要：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.

关 键 词：Location-based service(LBS) trajectory data mining offline-toonline(O2O)recommendation deep factorization machine(DeepFM) spatiotemporal context 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]