An end-to-end Machine Learning system for mitigating checkout abandonment in E-Commerce

• Autorzy: Rifat Md Rifatul Islam , Amin Md Nur , Munna Mahmud Hasan , Imran Abdullah Al

Identyfikatory

DOI

10.15439/2022F167

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Electronic Commerce (E-Commerce) has become one of the most significant consumer-facing tech industries in recent years. This industry has considerably enhanced people's lives by allowing them to shop online from the comfort of their own homes. Despite the fact that many people are accustomed to online shopping, e-commerce merchants are facing a significant problem, a high percentage of checkout abandonment. In this study, we have proposed an end-to-end Machine Learning (ML) system that will assist the merchant to minimize the rate of checkout abandonment with proper decision making and strategy. As a part of the system, we developed a robust machine learning model that predicts if someone will checkout the products added to the cart based on the customer's activity. Our system also provides the merchants with the opportunity to explore the underlying reasons for each single prediction output. This will indisputably help the online merchants in business growth and effective stock management.

Słowa kluczowe

EN

E-Commerce; check-out prediction; check-out abandonment; decision support; explainable AI; XAI; LIME

PL

E-commerce; handel elektroniczny; wspomaganie decyzji; wytłumaczalna sztuczna inteligencja; XAI; LIME

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 129--132
• Opis fizyczny: Bibliogr. 14 poz., il., tab., wykr.

Twórcy

autor

Rifat Md Rifatul Islam

• Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh

autor

Amin Md Nur

• University Jean Monnet, Saint Etinne, France

autor

Munna Mahmud Hasan

• Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh

autor

Imran Abdullah Al

• American International University-Bangladesh, Dhaka, Bangladesh

Bibliografia

• 1. Staista,https://www.statista.com/statistics/379046/ worldwide-retail-e-commerce-sales. Last accessed 8 Apr 2022
• 2. M. H. Munna, M. R. I. Rifat and A. S. M. Badrudduza, “Sentiment Analysis and Product Review Classification in E-commerce Platform,” 2020 23rd International Conference on Computer and Information Technology (ICCIT), 2020, pp. 1-6, http://dx.doi.org/10.1109/ICCIT51783.2020.9392710.
• 3. Al Imran, A. and Amin, M.N., 2020. Predicting the return of orders in the e-commerce industry accompanying with model interpretation. Procedia Computer Science, 176, pp.1170-1179. http://dx.doi.org/10.1016/j.procs.2020.09.113
• 4. Urbanke, P., Kranz, J. and Kolbe, L., 2015. Predicting product returns in e-commerce: the contribution of mahalanobis feature extraction.
• 5. Baymard Institute, https://baymard.com/lists/cart-abandonment-rate. Last accessed 8 Apr 2022
• 6. Mou, J., Cohen, J., Dou, Y. and Zhang, B., 2017. Predicting Buyers’repurchase Intentions in Cross-Border E-Commerce: A Valence Framework Perspective.
• 7. Budnikas, G., 2015. Computerised recommendations on e-transaction finalisation by means of machine learning. Statistics in Transition. New Series, 16(2), pp.309-322.
• 8. Cox, N.J., 2010. Speaking Stata: The limits of sample skewness and kurtosis. The Stata Journal, 10(3), pp.482-495. DOI: 10.1177/1536867X1001000311
• 9. Chen, T. and Guestrin, C., 2016, August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). http://dx.doi.org/10.1145/2939672.2939785
• 10. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.Y., 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
• 11. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. and Gulin, A., 2018. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.
• 12. Arik, S.Ö. and Pfister, T., 2021, May. Tabnet: Attentive interpretable tabular learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 8, pp. 6679-6687).
• 13. Feng, Ji, Yang Yu, and Zhi-Hua Zhou. “Multi-layered gradient boosting decision trees.” Advances in neural information processing systems 31 (2018).
• 14. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016) http://dx.doi.org/10.1145/2939672.2939778

Uwagi

1. Short article

2. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).