Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use
Ling Tang 1
Anying Wang 2
Zhenjing Xu 2
Jian Li 3  
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School of Economics and Management, Beihang University, Beijing, CHINA
Beijing University of Chemical Technology, Beijing, CHINA
Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing, CHINA
Online publish date: 2017-11-22
Publish date: 2017-11-22
EURASIA J. Math., Sci Tech. Ed 2017;13(12):7967–7983
This article belongs to the special issue "Problems of Application Analysis in Knowledge Management and Science-Mathematics-Education".
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.
Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing research, 37(3), 363-375.
Baesens, B., Viaene, S., & Van den Poel, D. (2002). Bayesian neural network learning for repeat purchase modelling in direct marketing. European Journal of Operational Research, 138(1), 191-211.
Bakos, J. Y. (1997). Reducing buyer search costs: Implications for electronic marketplaces. Management science, 43(12), 1676-1692.
Bastı, E., Kuzey, C., & Delen, D. (2015). Analyzing initial public offerings’ short-term performance using decision trees and SVMs. Decision Support Systems, 73, 15-27.
Bloch, P. H., Sherrell, D. L., & Ridgway, N. M. (1986). Consumer search: An extended framework. Journal of Consumer Research, 13(1), 119-126.
Boroujerdi, E. G., Mehri, S., Garmaroudi, S. S., Pezeshki, M., Mehrabadi, F. R., Malakouti, S., & Khadivi, S. (2014). A study on prediction of user’s tendency toward purchases in websites based on behavior models. In Proceedings of the 6th IEEE Conference on Information and Knowledge Technology, 61–66.
Buckinx, W., Verstraeten, G., & Van den Poel. D. (2007). Predicting customer loyalty using the internal transactional database. Expert systems with applications, 32(1), 125-134.
Bucklin, R. E., Lattin, J. M. Ansari, A. Gupta, S., Bell, D., Coupey, E., …, & Steckel, J. (2002). Choice and the Internet: From clickstream to research stream. Marketing Letters, 13(3), 245-258.
Chen, Z. Y., Fan, Z. P., & Sun, M. (2012). A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of operational research, 223(2), 461-472.
Close, A. G., & Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers’ online shopping cart use. Journal of Business Research, 63(9), 986-992.
Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4), 309-347.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Edwards, T. C., Cutler, D. R., & Zimmermann, N. E. (2006). Effects of sample survey design on the accuracy of classification tree models in species distribution models. Ecological Modelling, 199(2), 132-141.
Gupta, R., & Pathak, C. (2014). A machine learning framework for predicting purchase by online customers based on dynamic pricing. Procedia Computer Science, 36, 599-605.
Huang, C. L., & Dun, J. F. (2008). A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Applied Soft Computing, 8(4), 1381-1391.
Iwanaga, J., Nishimura, N., Sukegawa, N., & Takano, Y. (2016). Estimating product-choice probabilities from recency and frequency of page views. Knowledge-Based Systems, 99, 157-167.
Janiszewski, C. (1998). The influence of display characteristics on visual exploratory search behavior. Journal of Consumer Research, 25(3), 290-301.
Kazem, A., Sharifi, E., & Hussain, F. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing, 13(2), 947-958.
Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research, 201(3), 838-846.
Lee, K. C., & Kwon, S. (2008). Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: A causal map approach. Expert Systems with Applications, 35(4), 1567-1574.
Lemon, K. N., White, T. B., & Winer, R. S. (2002). Dynamic customer relationship management: Incorporating future considerations into the service retention decision. Journal of marketing, 66(1), 1-14.
Lessmann, S., Sung, M. C., & Johnson, J. E. V. (2009). Identifying winners of competitive events: A SVM-based classification model for horserace prediction. European Journal of Operational Research, 196(2), 569-577.
Lin, S. W., Lee, Z. J., & Chen, S. C. (2008). Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied Soft Computing, 8(4), 1505-1512.
Lin, S. W., Lee, Z. J., Chen, S. C., & Tseng, T. Y. (2008). Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied Soft Computing, 8(4), 1505-1512.
Lin, S. W., Ying, K. C., Chen, S. C., & Lee, Z. J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert systems with applications, 35(4), 1817-1824.
Liu, L. W., Chang, C. M., Huang, H. C., & Chang, Y. L. (2016). Verification of social network site use behavior of the university physical education students. Eurasia Journal of Mathematics, Science & Technology Education, 12(4), 793-805.
Łukasik, S., & Żak, S. (2009). Firefly algorithm for continuous constrained optimization tasks. International Conference on Computational Collective Intelligence. Springer Berlin Heidelberg, 97-106.
Mandal, P., Haque, A. U., Meng, J., Srivastana, A. K., & Martinez, R. (2013). A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting. IEEE Transactions on Power Systems, 28(2), 1041-1051.
Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European journal of operational research, 183(3), 1466-1476.
Martin-Barragan, B., Lillo, R., & Romo, J. (2014). Interpretable support vector machines for functional data. European Journal of Operational Research, 232(1), 146-155.
Moe, W. W, Chipman, H., George, E. I., & McCulloch, R. (2002). A Bayesian treed model of online purchasing behavior using in-store navigational clickstream. Revising for 2nd review at Journal of Marketing Research.
Moe, W. W., & Fader, P. S. (2004). Dynamic conversion behavior at e-commerce sites. Management Science, 50(3), 326-335.
Moe, W. W., & Fader, P. S. (2004b). Capturing evolving visit behavior in clickstream data. Journal of Interactive Marketing, 18(1), 5-19.
Montgomery, A. L., Li, S., Srinivasan, K., Liechty, J. C. (2004). Modeling online browsing and path analysis using clickstream data. Marketing Science, 23(4), 579-595.
Padmanabhan, B., Zheng, Z., & Kimbrough, S. O. (2001). Personalization from incomplete data: What you don’t know can hurt. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 154-163.
Polat, K., & Güneş, S. (2007). Breast cancer diagnosis using least square support vector machine. Digital Signal Processing, 17(4), 694-701.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
Sun, A., Lim, E. P., & Liu, Y. (2009). On strategies for imbalanced text classification using SVM: A comparative study. Decision Support Systems, 48(1), 191-201.
Tang, L., Wang, S., & He, K. (2015). A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Annals of Operations Research, 234(1), 111-132.
Tang, L., Wang, Z., Li, X., Yu, L., & Zhang, G. (2015). A novel hybrid FA-Based LSSVR learning paradigm for hydropower consumption forecasting. Journal of Systems Science and Complexity, 28(5), 1080-1101.
Tang, L., Yu, L., & He, K. (2014). A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting. Applied Energy, 128, 1-14.
Tang, L., Yu, L., Wang, S., Li, J., & Wang, S. (2012). A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Applied Energy, 93, 432-443.
Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behavior. European Journal of Operational Research, 166(2), 557-575.
Weng, S. S., Liu, S. C., & Wu, T. H. (2011). Applying Bayesian network and association rule analysis for product recommendation. International Journal of Electronic Business Management, 9(2), 149.
Wong, W. T., & Hsu, S. H. (2006). Application of SVM and ANN for image retrieval. European Journal of Operational Research, 173(3), 938-950.
Wu, C., & Chen, H. L. (2000). Counting your customers: Compounding customer’s in-store decisions, interpurchase time and repurchasing behavior. European Journal of Operational Research, 127(1), 109-119.
Wu, W. C., & Perng, Y. H. (2016). Research on the Correlations among Mobile Learning Perception, Study Habits, and Continuous Learning. Eurasia Journal of Mathematics, Science & Technology Education, 12(6).
Xie, W., Yu, L., & Xu, S. (2006). A new method for crude oil price forecasting based on support vector machines. Lecture Notes in Computer Science, 3994, 444-451.
Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-Inspired Computation, 2(2), 78-84.
Yu, L., Wang, S., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623-2635.
Yu, L., Yue, W., Wang, S., Lai, K. K. (2010). Support vector machine based multiagent ensemble learning for credit risk evaluation. Expert Systems with Applications, 37(2), 1351-1360.