Enhancing E-Commerce Recommendation Systems Using Big Data Analytics by Analyzing Customer Engagement Factors
Keywords:
Customer Engagement, Data-Driven Analytics, E-Commerce, Recommendation Systems, Personalization, Predictive ModelingAbstract
ABSTRACT - This research explores the enhancement of e-commerce recommendation systems through Big Data analytics with a particular focus on understanding and leveraging customer engagement factors. The study aimed to identify the key drivers of consumer interaction, assess the impact of integrating Big Data analytics and develop a predictive model to improve the accuracy and relevance of personalized recommendations. A mixed-methods approach was employed, combining quantitative analysis of digital behavioral data with qualitative insights gathered through surveys. This integration enabled the development and robust validation of a Big Data-driven predictive model for personalized e-commerce recommendations. The findings show that combining digital behavioral data with consumer perceptions significantly enhances predictive accuracy as evidenced by improved error metrics (MAE, RMSE), higher explanatory power (R2) and stronger classification outcomes (precision, recall, AUC-ROC). Click frequency, session duration and perceived recommendation accuracy emerged as key predictors of engagement while data triangulation confirmed the model’s reliability. In conclusion, this study demonstrates the value of Big Data-driven personalization in e-commerce, offering practical benefits such as user engagement, marketing efficiency and conversion rates. Academically, it advances knowledge on predictive modeling and recommendation systems, underscoring the effectiveness of mixed methods and advanced analytics. Future research should examine refinements and the long-term impact of dynamic personalization strategies.
INDEX TERMS - Big Data Analytics, Customer Engagement, E-Commerce Recommendation Systems, Personalization, Predictive Modeling
References
J. Q. Anderson, “Recommender systems in e-commerce: An introductory study,” Journal of Retailing, vol. 75, no. 2, pp. 210–228, 1999.
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An open architecture for collaborative filtering of netnews,” in Proc. ACM Conf. Computer Supported Cooperative Work (CSCW), 1994, pp. 175–186.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009. https://doi.org/10.1109/MC.2009.263
R. Bell and T. Koren, “Lessons from the Netflix Prize challenge,” ACM SIGKDD Explorations Newsletter, vol. 9, no. 2, pp. 75–79, 2007.
J. Bennett and S. Lanning, “The Netflix Prize,” in Proc. KDD Cup and Workshop, 2007.
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005. https://doi.org/10.1109/TKDE.2005.99
C. C. Aggarwal, Recommender Systems: The Textbook, 2nd ed. Cham, Switzerland: Springer, 2016.
P. G. Campos, F. J. Díez, and I. Cantador, “Context-aware recommender systems: A survey of the state of the art,” Knowledge-Based Systems, vol. 69, pp. 14–28, 2014.
Y. Chen et al., “Analysis of click-stream data for user behavior modeling,” Journal of Information Science, vol. 44, no. 3, pp. 345–357, 2018.
B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA, USA: Morgan & Claypool Publishers, 2012.
A. McAfee and E. Brynjolfsson, “Big data: The management revolution,” Harvard Business Review, vol. 90, no. 10, pp. 60–68, 2012.
I. A. T. Hashem et al., “The rise of big data on cloud computing: Review and open research issues,” Information Systems, vol. 47, pp. 98–115, 2015. https://doi.org/10.1016/j.is.2014.07.006
T. Li et al., “A review of deep learning in recommender systems,” Journal of Information Systems, vol. 34, no. 1, pp. 1–20, 2020.
D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 5th ed. Hoboken, NJ, USA: Wiley, 2012.
R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 6th ed. Upper Saddle River, NJ, USA: Pearson Prentice Hall, 2007.
I. T. Jolliffe, Principal Component Analysis, 2nd ed. New York, NY, USA: Springer, 2002.
B. S. Everitt, Cluster Analysis, 5th ed. Chichester, UK: Wiley, 2011.
L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
D. A. Dillman, Mail and Internet Surveys: The Tailored Design Method. Hoboken, NJ, USA: John Wiley & Sons, 2000.
J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th ed. Thousand Oaks, CA, USA: Sage Publications, 2017.
R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proc. Int. Joint Conf. Artif. Intell. (IJCAI), 1995, pp. 1137–1145.
D. A. Dillman, J. D. Smyth, and L. M. Christian, Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method, 4th ed. Hoboken, NJ, USA: John Wiley & Sons, 2014.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009.
A. Bryman, Social Research Methods, 5th ed. Oxford, UK: Oxford University Press, 2016.
J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.
G. A. Churchill and D. Iacobucci, Marketing Research: Methodological Foundations, 9th ed. Mason, OH, USA: South-Western Cengage Learning, 2006.
A. Field, Discovering Statistics Using IBM SPSS Statistics, 4th ed. Thousand Oaks, CA, USA: Sage Publications, 2013.
G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ, USA: Wiley, 2015.
J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 5–53, 2004.
J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. New York, USA: Routledge, 1988.
M. Zaharia et al., “Spark: Cluster computing with working sets,” 2010.
C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE),” Climate Research, vol. 30, pp. 79–82, 2005.
X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in Artificial Intelligence, vol. 2009, pp. 1–19, 2009. https://doi.org/10.1155/2009/421425
R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331–370, 2002. https://doi.org/10.1023/A:1021240730564
G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, 2003. https://doi.org/10.1109/MIC.2003.1167344
G. Adomavicius et al., “Context-aware recommender systems,” AI Magazine, vol. 32, no. 3, p. 67, 2011.
P. Zikopoulos and C. Eaton, Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data. New York, NY, USA: McGraw-Hill, 2012.
E. Rahm, “Data cleaning: Problems and current approaches,” 2000.
L. Baltrunas et al., “Context-aware places of interest recommendations for mobile users,” Lecture Notes in Computer Science, vol. 6769, pp. 531–540, 2011.
M. Wedel and W. A. Kamakura, Market Segmentation: Conceptual and Methodological Foundations. New York, NY, USA, 2000.
U. Shardanand and P. Maes, “Social information filtering,” 1995.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” arXiv, 2014. https://arxiv.org/abs/1409.3215
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
N. Peña-García et al., “Purchase intention and purchase behavior online,” Heliyon, vol. 6, no. 6, 2020.
J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Communications of the ACM, vol. 51, pp. 107–113, 2008.
T. Chai and R. R. Draxler, “Root mean square error or mean absolute error?,” Geoscientific Model Development, vol. 7, no. 3, pp. 1247–1250, 2014. https://doi.org/10.5194/gmd-7-1247-2014
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 G Dilshan, UC Wickramarathne, MG Jayawickrama, SS Tissera
This work is licensed under a Creative Commons Attribution 4.0 International License.
