A Data-Driven Review of Machine Learning Techniques for E-commerce Product Recommendation Systems

Muhammad Rizwan Tahir; Nouman Nazir; Kashif Ishaq; Shakeel Ahmed

Authors

Muhammad Rizwan Tahir Department of Artificial Intelligence, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Nouman Nazir Department of Artificial Intelligence, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Kashif Ishaq Department of Informatics and Systems, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Shakeel Ahmed Department of Artificial Intelligence, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

Keywords:

Recommender Systems, E-commerce, Machine Learning, Hybrid Recommendation

Abstract

In today’s digital economy, recommendation systems are essential for enhancing customer experience and driving e-commerce growth. This study presents a comparative, quality-ranked review of machine learning-based product recommendation techniques, evaluating five key approaches: association rule mining, content-based filtering, collaborative filtering, knowledge-based systems, and hybrid models. Using a systematic literature review of 44 peer-reviewed publications across major publishers, the analysis includes geographic and publisher-wise trends and a structured quality assessment rubric. Results highlight hybrid systems as the most promising strategy, offering superior accuracy, diversity, and personalization while addressing cold-start, sparsity, and scalability challenges. Each technique’s strengths, limitations, and practical deployment considerations are critically examined to support evidence-based decision-making. The study concludes by recommending hybrid approaches tailored to domain-specific needs, offering actionable insights for both researchers and industry practitioners seeking effective and adaptable recommendation systems.

References

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., vol. 46, pp. 109–132, 2013, doi: https://doi.org/10.1016/j.knosys.2013.03.012.

T. M. K. Xiaoyuan Su, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., 2009, doi: https://doi.org/10.1155/2009/421425.

D. C. Gema Bello-Orgaz, Jason J. Jung, “Social big data: Recent achievements and new challenges,” Inf. Fusion, vol. 28, pp. 45–59, 2016, doi: https://doi.org/10.1016/j.inffus.2015.08.005.

J. A. K. Michael D. Ekstrand, John T. Riedl, “Collaborative Filtering Recommender Systems,” Found. Trends® Human–Computer Interact., vol. 4, no. 2, pp. 81–173, 2011, doi: http://dx.doi.org/10.1561/1100000009.

S. Sedhain, A. K. Menony, S. Sannery, and L. Xie, “AutoRec: Autoencoders meet collaborative filtering,” WWW 2015 Companion - Proc. 24th Int. Conf. World Wide Web, pp. 111–112, May 2015, doi: 10.1145/2740908.2742726;SUBPAGE:STRING:ABSTRACT;CSUBTYPE:STRING:CONFERENCE.

F. Ricci, L. Rokach, and B. Shapira, “Recommender Systems Handbook: Third Edition,” Recomm. Syst. Handb. Third Ed., pp. 1–1060, Jan. 2022, doi: 10.1007/978-1-0716-2197-4.

J. J. K. Qi An, Saifur Rahman, Jingwen Zhou, “A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges,” Sensors, vol. 23, no. 9, p. 4178, 2023, doi: https://doi.org/10.3390/s23094178.

B. Thomas and A. K. John, “Machine Learning Techniques for Recommender Systems – A Comparative Case Analysis,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1085, no. 1, p. 012011, Feb. 2021, doi: 10.1088/1757-899X/1085/1/012011.

K. A. J. Mingming Guo, Nian Yan, Xiquan Cui, San He Wu, Unaiza Ahsan, Rebecca West, “Deep Learning-based Online Alternative Product Recommendations at Scale,” Assoc. Comput. Linguist., pp. 19–23, 2020, doi: 10.18653/v1/2020.ecnlp-1.3.

S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Comput. Surv., vol. 52, no. 1, Jan. 2019, doi: 10.1145/3285029;REQUESTEDJOURNAL:JOURNAL:CSUR;TAXONOMY:TAXONOMY:ACM-PUBTYPE;PAGEGROUP:STRING:PUBLICATION.

L. Tian, B. Yang, X. Yin, and Y. Su, “A Survey of Personalized Recommendation Based on Machine Learning Algorithms,” ACM Int. Conf. Proceeding Ser., pp. 602–610, Nov. 2020, doi: 10.1145/3443467.3444711;SUBPAGE:STRING:ABSTRACT;CSUBTYPE:STRING:CONFERENCE.

M.-F. Leung et al., “A portfolio recommendation system based on machine learning and big data analytics,” Data Sci. Financ. Econ. 2023 2152, vol. 3, no. 2, pp. 152–165, 2023, doi: 10.3934/DSFE.2023009.

M. E. G. Manal Loukili, Fayçal Messaoudi, “Machine learning based recommender system for e-commerce,” Inst. Adv. Eng. Sci., vol. 12, no. 4, 2023, doi: http://doi.org/10.11591/ijai.v12.i4.pp1803-1811.

M. Tahir, R. N. Enam, and S. M. N. Mustafa, “E-commerce platform based on Machine Learning Recommendation System,” IMTIC 2021 - 6th Int. Multi-Topic ICT Conf. AI Meets IoT Towar. Next Gener. Digit. Transform., 2021, doi: 10.1109/IMTIC53841.2021.9719822.

A. A. Ssvr Kumar Addagarla, “Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce,” Symmetry (Basel)., vol. 12, no. 11, p. 1783, 2020, doi: https://doi.org/10.3390/sym12111783.

J. Anitha and M. Kalaiarasu, “Optimized machine learning based collaborative filtering (OMLCF) recommendation system in e-commerce,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 6, pp. 6387–6398, Jun. 2021, doi: 10.1007/S12652-020-02234-1/METRICS.

R. Sharma, S. Rani, and S. Tanwar, “Machine learning algorithms for building recommender systems,” 2019 Int. Conf. Intell. Comput. Control Syst. ICCS 2019, pp. 785–790, May 2019, doi: 10.1109/ICCS45141.2019.9065538.

D. J. Dudhia, S. R. Dave, and S. Yagnik, “Self attentive product recommender - A hybrid approach with machine learning and neural network,” 2020 Int. Conf. Emerg. Technol. INCET 2020, Jun. 2020, doi: 10.1109/INCET49848.2020.9154034.

A. Biswas, K. S. Vineeth, A. Jain, and Mohana, “Development of Product Recommendation Engine by Collaborative Filtering and Association Rule Mining Using Machine Learning Algorithms,” Proc. 4th Int. Conf. Inven. Syst. Control. ICISC 2020, pp. 272–277, Jan. 2020, doi: 10.1109/ICISC47916.2020.9171210.