題 目： Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use
主講人： 李健
時 間：2017年12月13日上午10:00
地 點：主樓418房間
主講人介紹：
李健，北京工業(yè)大學經濟與偉德國際1946bv官網教授。研究方向：物流與供應鏈管理、安全與應急管理。入選2012年度教育部新世紀優(yōu)秀人才支持計劃，加拿大溫莎大學訪問學者，兼任中國指揮與控制學會安全防護與應急管理專業(yè)委員會總干事、中國系統(tǒng)工程學會監(jiān)事會監(jiān)事、湖南圖靈危化品儲運安全技術研究院智慧物流與智慧供應鏈管理實驗室主任等。在Omega、IJPE等國內外主流重點期刊發(fā)表論文50余篇，出版英文專著 2 部。主持國家自然科學基金項目3項，參加國家重點研發(fā)計劃1項，參加國家自然科學基金重點項目1項。
內容介紹：
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。
（承辦：管理工程系，科研與學術交流中心）
