题目： Calibrating the Helpfulness of Online Product Reviews: An Iterative Bayesian Probability Approach
主讲人： 郭迅华（清华大学）
时间：2017年12月26日（周二）上午10：00
地点：主楼418

主讲人介绍：
       2000年获得清华大学管理信息系统专业学士学位及计算机科学与技术专业学士学位，2005年获得清华大学管理科学与工程专业硕士学位和博士学位。现任清华大学经济12BET副教授，主要研究领域为管理信息系统、电子商务、社会网络、商务智能。讲授课程包括管理信息系统、信息技术与组织、计算机系统原理、计算机网络。学术论文发表于MIS Quarterly、Journal of MIS、Communications of the ACM、DecisionSciences、INFORMS Journal on Computing、Information Systems Journal、Journal of Information Technology、Information Sciences、Information & Management、Decision Support Systems、Computers in Human Behavior、ACM Transactions on Knowledge Discovery from Data等信息系统领域重要国际期刊，以及《管理科学学报》、《管理世界》、《中国管理科学》、《系统工程理论与实践》等重要国内期刊，作为负责人或骨干参与了多项国家自然科学基金项目和企业项目。曾获得清华大学学术新秀、优秀博士毕业生荣誉称号。曾于2008年在德国RWTH Aachen University
做访问学者以及在MIT斯隆12BET担任国际教职研究员。现任国际信息系统协会中国分会（CNAIS）常务理事兼副秘书长，《信息系统学报》主编助理，Electronic Commerce Research、Journal of Global Information Management等国际学术杂志编委会成员。

内容介绍：
       Voting mechanisms are widely adopted for evaluating the quality and reputation of user generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels.  Existing methods are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Furthermore, an out-of-sample user study is conducted on Amazon Mechanical Turk as well as in a university lab. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with the novel approach that may be adapted to a wide range of research topics such as recommender systems and social media analytics.

（承办：管理工程系，科研与学术交流中心）