面向Online-to-Offline智能商务的大数据融合与应用

Online-to-Offline (O2O) Fusion is a new-emerging paradigm that will promote the further transformation of e-business. It is also meaningful for both online enterprise and retail enterprise to break through the bottlenecks of traditional business models that they have suffered. Currently, O2O research mainly focuses on discussing paradigm or strategies itself. Nevertheless, how to release the intelligence of O2O fusion remains a pretty much open issue. Although the penetration of big data computing technologies can provide significant supports to O2O intelligent business decision, there are still a multitude of challenges that are worth exploring, including models are not perfect enough to support mining heterogeneous data, fragmented knowledge is difficult to be effectively fused, and the storage and computing infrastructure is not clear enough for business big data. In light of this, we organize a multidisciplinary team from the fields of data mining, database, behavior analysis, and business intelligence to explore several crucial issues within O2O big data fusion and its applications. In particular, we will first build a big data mining framework for O2O intelligence, meeting the challenges rising from data decentralization and heterogeneity. We then focus on designing novel mining and fusion methods for big trajectory data and fragmented knowledge. Last but not least, we will design and develop a real platform for O2O big data mining and construct several demonstration applications. We expect to publish a series of high-quality research papers during the project execution. As a result, this declared project would be expected to replenish and promote both theoretical research and practical applications of big data.

Online-to-Offline (O2O)融合是推动电子商务发生深层次变革的新兴模式，对线上企业突破商业模式瓶颈及传统零售业转型发展具有重大意义。目前，O2O商务领域的研究仍然停留在模式和策略的探讨上，如何释放O2O融合所带来的智能性尚未得到充分的探索。大数据理念的渗透普及、及大数据计算技术的瞬息千里，为实现O2O智能商务决策提供了卓越支撑。然而，已有的面向智能商务的大数据挖掘研究存在建模方法不够完善、碎片化知识难以有效融合、存储和计算架构不够明晰等问题。为此，项目组联合来自多个交叉领域的学者，围绕“面向O2O智能商务的大数据融合与应用”展开全面研究。项目将构建O2O商务大数据融合框架，突破轨迹大数据挖掘方法及碎片化知识融合方法，并构建O2O商务大数据分析平台及示范应用。项目有望在O2O商务大数据挖掘方法上产生一批国际水平的研究成果，对大数据研究及应用提供重要的补充和推动作用。

本项目以O2O电子商务决策支持为导向，构建O2O商务大数据融合框架，突破轨迹大数据挖掘方法，深入支持 O2O一体化的多渠道知识融合方法。本项目主要成果内容包括：.1）在苏宁易购部署传感器采集了1500万条移动轨迹数据，并基于该数据集实现了室内定位算法；同时，针对多源异构大数据，提出了模糊组合学习框架、多任务聚类学习以及交互式位置的Web索引统一处理算法；.2）提出了轨迹的逆k近邻查询、异常轨迹检测、代表性轨迹查询、空间对象推荐以及线下广告牌投放，为有效融合线下实体店监测信息提供模型支撑；.3）提出了线上线下群体行为分析、异常轨迹检测、属性图聚类、社区隐藏框架、大规模图聚类框架以及融入多侧面特征的旅游推荐方法，为O2O新型商务应用提供理论和算法支撑；.4）提出了虚假评论发布者检测框架、轨迹驱动的广告牌投放策略、基于多任务学习的粮食产后损失预测模型、旅游包推荐算法以及用户购买行为预测，在实践中验证理论研究的可用性和有效性。.本项目的研究在行为模式挖掘，及复杂网络与行为特征的融合学习上获得了一定的理论突破，从而形成了以下几个方面的成果: 1）项目组共发表（含录用）期刊论文91篇，其中SCI收录40篇，包含17篇IEEE/ACM汇刊论文：TKDE、TCyb、TFS、TKDD、TII、TSMCS、TWeb、TIST等，及国内期刊《中国科学（信息科学）》、《计算机学报》、《计算机研究与发展》3篇；2）发表国际会议论文 42 余篇，包含SIGIR、SIGKDD、IJCAI、SIGMOD、ICDE等CCF A类会议20 篇，CIKM、 WSDM、DASFAA、ICWS等CCF B类会议10篇；3）发表 Springer 短著1本，及2篇综述性图书章节；4）2018年获得江苏省科技进步二等奖；5）获得国家发明专利授权11项； 4）培养研究生24名。