题    目： Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use
主讲人： 李健
时    间：2017年12月13日上午10:00
地    点：主楼418房间

主讲人介绍：
       李健，北京工业大学经济与12BET教授。研究方向：物流与供应链管理、安全与应急管理。入选2012年度教育部新世纪优秀人才支持计划，加拿大温莎大学访问学者，兼任中国指挥与控制学会安全防护与应急管理专业委员会总干事、中国系统工程学会监事会监事、湖南图灵危化品储运安全技术研究院智慧物流与智慧供应链管理实验室主任等。在Omega、IJPE等国内外主流重点期刊发表论文50余篇，出版英文专著 2 部。主持国家自然科学基金项目3项，参加国家重点研发计划1项，参加国家自然科学基金重点项目1项。

内容介绍：
       Due to the complexity of the e-commerce system, a hybrid model for online-purchasing behavior forecasting is developed to predict whether or not a customer makes a purchase during the next visit to the online store based on the previous behaviors, i.e., online-purchasing behavior. The proposed model makes contributions to literature from two perspectives: (1) a classification model is proposed based on the “hybrid modeling” concept, in which an effective artificial intelligence (AI) technique of support vector machine (SVM) is employed for classification forecasting and further extended by introducing the promising AI optimization tool of firefly algorithm (FA), to solve the crucial but tough task of parameters selection, i.e., the FA-based SVM model; (2) an appropriate predictor set is carefully designed especially considering online shopping cart use which was otherwise neglected in existing models, apart from other common online behaviors, e.g., clickstream behavior, previous purchase behavior and customer heterogeneity. To verify the superiority of the proposed model, an online furniture store is focused on as study sample, and the empirical results statistically support that the proposed FA-based SVM model considering online shopping cart use significantly beat all benchmarking models (with other popular classification methods and/or different predictor sets) in terms of prediction accuracy。

（承办：管理工程系，科研与学术交流中心）