认知与智能信息处理实验室新学期首篇产学研合作论文“An Efficient Passenger-Hunting Recommendation Framework with Multi-Task Deep Learning”被‍物联网领域顶级期刊IEEE Internet of Things Journal（中科院一区，五年IF：8.385）录用。

该论文华南师范大学为第一完成单位，是与“DataGrand（达观数据）”合作完成（论文第一作者黄震华老师目前为DataGrand技术顾问）。目前该论文的研究成果正与智能交通领域的企业商谈应用和推广。

DataGrand：推荐系统、自然语言处理和深度学习领域上海市重点支持企业，2017年获“未来独角兽TOP30”，2018年获中国最具潜力企业奖。目前，达观团队由来自腾讯、盛大、百度、阿里等知名企业的高管和技术专家组成，曾经多次荣获ACM国际数据挖掘竞赛冠军，申请六十余项发明专利，出版两本人工智能著作和数百篇技术论文。达观数据是微软加速器、联想之星、SAP创新营的成员，并先后获得了真格基金、软银赛富、方广资本等著名机构的数亿元投资。

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Abstract—Using large-scale GPS trajectory data to improve taxi services has recently attracted much attention in Internet of Things and smart city communities. In this paper, we use a large-scale GPS trajectory dataset generated by over 12,000 taxis in a period of three months in Shanghai, China, and present an efficient passenger-hunting recommendation framework with the multi-task deep learning paradigm. This framework contains two modules: offline training of passenger-hunting recommendation model (OT-PHRM) and online application of passenger-hunting recommendation model (OA-PHRM). The module OT-PHRM mainly includes two DCNNs (Deep Convolutional Neural Networks) and uses the multi-task learning strategy. The first DCNN realizes the region prediction for picking up passengers, while the second DCNN uses the weight-sharing structure to predict the levels of road congestion and earnings of carrying passengers. In particular, for the input of two DCNNs, we not only consider contextual features of taxi driving, region features and valuable statistical features, but also combine individual features into meaningful ones. In the module OA-PHRM, we propose DL-PHRec, which calculates three prediction values using two trained DCNNs in OT-PHRM in real time, and then recommends a personal ranking-list of regions to each taxi driver according to their scores. Experimental results show the feasibility and effectiveness of our recommendation framework.

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