題目： Calibrating the Helpfulness of Online Product Reviews: An Iterative Bayesian Probability Approach
主講人： 郭迅華（清華大學）
時間：2017年12月26日（周二）上午10：00
地點：主樓418
主講人介紹：
2000年獲得清華大學管理信息系統專業學士學位及計算機科學與技術專業學士學位，2005年獲得清華大學管理科學與工程專業碩士學位和博士學位。現任清華大學經濟偉德國際1946bv官網副教授，主要研究領域為管理信息系統、電子商務、社會網絡、商務智能。講授課程包括管理信息系統、信息技術與組織、計算機系統原理、計算機網絡。學術論文發表于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等信息系統領域重要國際期刊，以及《管理科學學報》、《管理世界》、《中國管理科學》、《系統工程理論與實踐》等重要國內期刊，作為負責人或骨干參與了多項國家自然科學基金項目和企業項目。曾獲得清華大學學術新秀、優秀博士畢業生榮譽稱號。曾于2008年在德國RWTH Aachen University
做訪問學者以及在MIT斯隆偉德國際1946bv官網擔任國際教職研究員。現任國際信息系統協會中國分會（CNAIS）常務理事兼副秘書長，《信息系統學報》主編助理，Electronic Commerce Research、Journal of Global Information Management等國際學術雜志編委會成員。
內容介紹：
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.
（承辦：管理工程系，科研與學術交流中心）
