Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use
Autor: | Jian Li, Zhenjing Xu, Ling Tang, Anying Wang |
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Rok vydání: | 2017 |
Předmět: |
Cart
Computer science business.industry Applied Mathematics 05 social sciences 02 engineering and technology E-commerce Benchmarking Machine learning computer.software_genre Purchasing Education Support vector machine 0502 economics and business 0202 electrical engineering electronic engineering information engineering 050211 marketing 020201 artificial intelligence & image processing Firefly algorithm Artificial intelligence business Hybrid model computer Clickstream |
Zdroj: | EURASIA Journal of Mathematics, Science and Technology Education. 13 |
ISSN: | 1305-8223 |
Popis: | 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. |
Databáze: | OpenAIRE |
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