C2C市场中基于行为树的销量识别与发布研究

电商交易平台发布的销量对消费者购买决策有很大影响力，致使电商企业设法提高其销量。识别并发布电商企业的真实销量是C2C市场健康发展的基础之一，但我国在这方面研究才刚起步，其紧迫性突显重要。本项目以刻画C2C市场中网购决策信息搜寻行为为切入点，研究电商企业的销量识别与发布方法。项目提炼C2C市场中消费者网购决策影响因素，刻画其影响因素与决策信息搜寻网页浏览行为的关系，构建消费者网购决策信息搜寻行为树模型；探究C2C市场中虚增销量的运作机制，建立虚增销量参与者的行为树模型；设计基于行为树相似度的虚增销量识别模型，以及基于D-S理论的销量发布方法，并在C2C交易平台对销量识别与发布方法进行验证。项目预期在网购决策信息搜寻行为的刻画、C2C市场中的销量识别与发布方法等方面取得创新成果。相关研究成果有助于丰富C2C市场中销量识别和发布方法，对我国C2C市场中的宏观政策制定和微观管理都有重要的指导意义。

Sales volume online shopping distributed by e-commerce trading platform has great important influence to decision-making for online shopping clients and enables e-commerce enterprises to improve their sales volume by all means. The detection and publication of true sales volume for e-commerce enterprises is one of foundations for healthy development of C2C market, and research on it just begins and its pressing time is especially important in our country. Starting from describing information searching behaviors for online shopping decision-making in C2C market, this project will study identification and publication of sales online shopping. Our work will extract online shopping decision-making influence factors for online clients, characterize the relationship between online shopping decision-making influence factors and Web browsing behaviors for decision-making information searching online shopping, and establish Web browsing-path-based client decision-making information searching behavior-tree model, explore the operating mechanism and fraudster behavior characteristics for sales volume manipulation in C2C market, build Web browsing based behavior-tree model for fraudsters, and investigate the method for computing the similarity of behavior-trees, and construct model for detecting sales volume inflation based on behavior-tree similarity discrimination, and method for publishing sales volume online shopping based on D-S evidence theory and validate the method for detecting and publishing sales volume online shopping by mining transaction data from C2C trading platform. This proposal is expected that innovative results will be achieved for describing information searching behaviors for online shopping, detecting sales volume inflation and publishing method for sales volume in C2C market. The related research results are helpful to enrich sales volume detection model and method in C2C market,and have important guidance to macro-police and micro-management of C2C market in China.