本课题研究在线市场的匹配机制、声誉反馈的激励机制以及监管方式创新问题。一、在线市场的规模性和个性化特征使得仅靠搜索机制无法实现有效匹配，本课题拟结合在线商品市场的特点，提出和论证“分层匹配”机制是一种可行的途径，并准备进行在线受控实验的办法来验证和改进。二，在线市场中，由于卖家会对买家的声誉反馈行为进行干预，使得声誉机制运行不畅。本课题拟研究存在卖家干预的情况下平台企业如何对买家的反馈行为进行激励，并通过淘宝、京东和亚马逊等平台的声誉激励政策和数据进行对比分析，需求有效的声誉反馈激励机制；三、在线市场面临平台企业的私人监管和政府的公共监管，由此产生了在线市场的多种监管方式：1）平台单独监管；2）政府单独监管；3）政府监管平台、平台监管卖家的双重监管。我们拟对比研究这三种监管模式的效果，并运用网约车行业的数据进行检验，在此基础上，提出相关的监管政策建议。

The research focuses on three topics - the matchmaking mechanism, the incentive mechanism of reputation feedback, and innovation of regulations – of the online market. First, over dependence on the search mechanism cannot help achieve effective matching owing to the scale and personalised features of the online market. The study aims to explore and demonstrate that “delamination matching” is a feasible way to enhance the matching efficiency by considering the characteristics of the online commodity market, which is on the basis of some Internet trading platform under a controlled experiment for test and improvement. Second, In the online market, the seller is likely to intervene in the behaviour of the reputation feedback of the buyer, which may cause a poor performance of the reputation mechanism. The research tries to explain how the platform enterprises motivate the behaviour of the reputation feedback if there exists the seller’s intervention. A comparison analysis will be taken via collecting and analysing the reputation incentive policies and relevant data, e.g. Taobao, Jingdong, Amazon, etc.. Thrid, platform enterprises in the online market can engage in private regulations and thus extending several regulation forms: 1) individual regulation by the platform; 2) public regulations; and 3) dual supervision – the government regulates platforms, and platforms regulate the seller. We compare the effectiveness of these three regulation models by using the data of online transportation network companies (TNC) for the test, and list some recommendations for regulatory policies.