Integration of machine learning in e-commerce
A systematic literature review on consumer behavior prediction and product recommendation
DOI:
https://doi.org/10.60036/sg7wnx04Keywords:
Machine Learning, E-commerce, Consumer Behavior Prediction, Collaborative FilteringAbstract
This systematic literature review examines the integration of machine learning (ML) in e-commerce, focusing on consumer behavior prediction and product recommendation systems. Following PRISMA guidelines, we searched Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, identifying 1,247 records. After screening, 48 peer-reviewed articles (2019-2024) were included. This review makes three novel contributions: (1) a taxonomy of ML algorithms categorizing approaches by function (prediction vs. recommendation) and technique (supervised, unsupervised, deep learning); (2) a comparative analysis of algorithm performance across different e-commerce contexts; and (3) identification of specific research gaps requiring investigation. Findings reveal that hybrid recommendation systems combining collaborative filtering with deep learning achieve superior accuracy (mean improvement of 15-23% over single-method approaches), while gradient boosting methods (XGBoost, LightGBM) demonstrate the highest predictive performance for purchase behavior. Critical challenges include cold-start problems, data sparsity, algorithmic bias, and privacy concerns. We propose an integrative framework mapping ML technique to specific e-commerce applications and identify five priority areas for future research. Limitations include English-language restrictions and potential publication bias toward positive results.
References
Agrawal, R., Imielinski, T., & Swami, A. (1994). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216.
Bobadilla, J., Ortega, F., Hernando, A., & Gutierrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023). Bias and debias in recommender system: A survey. ACM Transactions on Information Systems, 41(3), 1-39.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of KDD, 785-794.
Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. Proceedings of RecSys, 191-198.
De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction. European Journal of Operational Research, 269(2), 760-772.
Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2020). Towards early purchase intention prediction in online session based retailing systems. Electronic Markets, 31, 697-715.
Fernandez-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers? Journal of Machine Learning Research, 15(1), 3133-3181.
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020). LightGCN: Simplifying graph convolution for recommendation. Proceedings of SIGIR, 639-648.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. Proceedings of WWW, 173-182.
Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2016). Session-based recommendations with recurrent neural networks. Proceedings of ICLR.
Koren, Y. (2009). Collaborative filtering with temporal dynamics. Proceedings of KDD, 447-456.
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, 41(4), 2065-2073.
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems. Recommender Systems Handbook, 73-105.
MacKenzie, I., Meyer, C., & Noble, S. (2013). How retailers can keep up with consumers. McKinsey.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35.
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Page, M. J., et al. (2021). The PRISMA 2020 statement. BMJ, 372, n71.
Pariser, E. (2011). The Filter Bubble. Penguin Press.
Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems. Expert Systems with Applications, 97, 205-227.
Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender Systems Handbook (3rd ed.). Springer.
Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. Proceedings of SIGIR, 253-260.
Statista. (2024). E-commerce worldwide. https://www.statista.com/topics/871/online-shopping/
Sun, F., et al. (2019). BERT4Rec: Sequential recommendation with transformers. Proceedings of CIKM, 1441-1450.
Verhoef, P. C., et al. (2021). Digital transformation: A multidisciplinary reflection. Journal of Business Research, 122, 889-901.
Voigt, P., & Von dem Bussche, A. (2017). The EU GDPR: A Practical Guide. Springer.
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning. ACM TIST, 10(2), 1-19.
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system. ACM Computing Surveys, 52(1), 1-38.
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Copyright (c) 2026 Abd. Rasyid Syamsuri, Rifki Arohman, Muhammad Renaldy Saputra, Muhammad Ikhlash, Sri Karyani Damanik (Author)
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