Ontology-based approach to predicting consumer purchasing behavior in e-commerce

The relevance of this study is determined by the need to improve the accuracy and interpretability of models for predicting consumer purchasing behavior in online stores. Existing machine learning methods demonstrate high performance; however, their effectiveness largely depends on the composition and structure of the feature space, which is typically formed empirically and does not reflect the causal relationships between user actions. This study aims to develop a purchasing behavior prediction method based on an ontological analysis of the e-commerce domain. A formalized approach is proposed for describing entities and their interrelations, providing a systematic construction of the feature space and enabling its scalability across various online stores. The gradient boosting algorithm CatBoost was employed as the machine learning tool, trained on data obtained from the Yandex.Metrica web analytics system. The proposed method was tested on five online stores with different thematic focuses. Experimental results demonstrated stable quality metrics, with F-scores ranging from 65 % to 83 %, confirming the applicability and reproducibility of the developed approach. The findings have practical significance for the development of intelligent decision support systems in e-commerce and can be utilized in designing scalable analytical platforms for predicting user activity and purchase conversion.

1. Sikelis K., Tsekouras G.E., Kotis K.I. Ontology-Based Feature Selection: A Survey. arXiv. URL: https://doi.org/10.48550/arXiv.2104.07720 [Accessed 18th January 2026].

2. Vicient C., Sánchez D., Moreno A. An Automatic Approach for Ontology-Based Feature Extraction from Heterogeneous Textual Resources. Engineering Applications of Artificial Intelligence. 2013;26(3):1092–1106. https://doi.org/10.1016/j.engappai.2012.08.002

3. Di Noia T., Magarelli C., Maurino A., Palmonari M., Rula A. Using ontology-based data summarization to develop semantics-aware recommender systems. In: The Semantic Web: 15th International Conference, ESWC 2018, 03–07 June 2018, Heraklion, Crete, Greece. Cham: Springer; 2018. P. 128–144. https://doi.org/10.1007/978-3-319-93417-4_9

4. Lu S., Ye Y., Tsui R., et al. Domain Ontology-based Feature Reduction for High Dimensional Drug Data and its Application to 30-Day Heart Failure Readmission Prediction. In: 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 20–23 October 2013, Austin, TX, USA. IEEE; 2013. P. 478–484. https://doi.org/10.4108/icst.collaboratecom.2013.254124

5. Abdollahi M., Gao X., Mei Y., Ghosh Sh., Li J. An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation. In: 2019 IEEE Congress on Evolutionary Computation, 10–13 June 2019, Wellington, New Zealand. IEEE; 2019. P. 119–126. https://doi.org/10.1109/CEC.2019.8790259

6. Kühl N., Mühlthaler M., Goutier M. Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media. Electronic Markets. 2020;30(2):351–367. https://doi.org/10.1007/s12525-019-00351-0

7. Heilman C.M., Kaefer F., Ramenofsky S.D. Determining the appropriate amount of data for classifying consumers for direct marketing purposes. Journal of Interactive Marketing. 2003;17(3):5–28. https://doi.org/10.1002/dir.10057

8. Kotis K.I., Vouros G.A., Spiliotopoulos D. Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations. The Knowledge Engineering Review. 2020;35. https://doi.org/10.1017/S0269888920000065

9. Wen Zh., Lin W., Liu H. Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream Data. Systems. 2023;11(5). https://doi.org/10.3390/systems11050255

10. Hesvindrati N., Aminuddin A., Mahadhni J., Pambudi A., Sudaryatno B. Behavior-Based Purchase Intent Prediction in E-Commerce: A Machine Learning Approach. International Journal of Current Science Research and Review. 2025;8(8):3970–3980. https://doi.org/10.47191/ijcsrr/V8-i8-03

11. Liu D., Huang H., Zhang H., Luo X., Fan Zh. Enhancing customer behavior prediction in e-commerce: A comparative analysis of machine learning and deep learning models. Applied and Computational Engineering. 2024;55(1):181–195. https://doi.org/10.54254/2755-2721/55/20241475

12. Abhichandani D., Vadrevu N.R.T., Doshi P., Shrivastava Sh. Predicting Online Purchases Using Six Machine Learning Models Based on Customer Demographics. In: 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), 09–11 July 2025, Tirunelveli, India. IEEE; 2025. P. 1787–1792. https://doi.org/10.1109/icdici66477.2025.11135228

13. Ayyadapu A.K.R., Saini P., Gupta P., et al. Fuzzy Logic and Machine Learning Hybrid Model for Influencing Consumer Purchasing Behavior in E-Commerce. In: 2025 International Conference on Computing Technologies & Data Communication, 04–05 July 2025, HASSAN, India. IEEE; 2025. P. 1–6. https://doi.org/10.1109/icctdc64446.2025.11158906

14. Li H. Research on Consumer Behavior Prediction Based on E-commerce Data Analysis. BCP Business & Management. 2023;49:106–110. https://doi.org/10.54691/bcpbm.v49i.5411

15. Htar T.T., Zaw M.M. Predicting Consumer Purchasing Behavior Using SVM and Random Forest Classification Methods. International Journal of Scientific Research in Engineering and Management. 2025;9(8). https://doi.org/10.55041/ijsrem51727

16. Huang W. Analysis of Promotional Online Shopping Behavior Based on Machine Learning. Highlights in Science, Engineering and Technology. 2023;56:65–72. https://doi.org/10.54097/hset.v56i.9817

17. Usha U.M., Swamy K.P.N. Anticipatory Modeling of Product Purchases. International Journal of Advanced Research in Science, Communication and Technology. 2024;4(1):137–143. https://doi.org/10.48175/ijarsct-19117

18. Chaudhuri N., Gupta G., Vamsi V., Bose I. On the platform but will they buy? Predicting customers' purchase behavior using deep learning. Decision Support Systems. 2021;149. https://doi.org/10.1016/j.dss.2021.113622

19. Bhutani P., Baranwal Sh.K., Jain S. Semantic Framework for Facilitating Product Discovery. In: ACI'21: Workshop on Advances in Computational Intelligence at ISIC 2021, 25–27 February 2021, Delhi, India. 2021. P. 30–36.

20. García M. del M.R., García-Nieto J., Aldana-Montes J.F. An ontology-based data integration approach for web analytics in e-commerce. Expert Systems with Applications. 2016;63:20–34. https://doi.org/10.1016/j.eswa.2016.06.034

21. Kim H. Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information. Sustainability. 2021;13(4). https://doi.org/10.3390/su13041722

22. Preece Ch., Rojas Gaviria P. An ontology of consumers as distributed networks: a question of cause and effect. Journal of Marketing Management. 2024;40(7-8):628–634. https://doi.org/10.1080/0267257X.2024.2346010

23. Esmeli R., Bader-El-Den M., Abdullahi H. Towards early purchase intention prediction in online session based retailing systems. Electronic Markets. 2020;31:697–715. https://doi.org/10.1007/s12525-020-00448-x

24. Liu Zh., Zhang Y., Abedin M.Z., et al. Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction. Journal of Retailing and Consumer Services. 2024;79. https://doi.org/10.1016/j.jretconser.2024.103854

25. Zhang W., Wang M. An improved deep forest model for prediction of e-commerce consumers' repurchase behavior. PLoS ONE. 2021;16(9). https://doi.org/10.1371/journal.pone.0255906

26. Zhou Sh., Hudin N.S. Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and Graph Neural Network-Enhanced user profiling. PLoS ONE. 2024;19(4). https://doi.org/10.1371/journal.pone.0299087

27. Chauleva B., Capeska Bogatinoska D., Karadimce A. Optimizing Customer Journey through Advanced Analytics Techniques over Google Analytics 4 Data in Google BigQuery. WSEAS Transactions On Computers. 2024;23:336–346. https://doi.org/10.37394/23205.2024.23.33

28. Svyatov R.S. Forecasting e-commerce user purchase behavior based on event data. Modeling, Optimization and Information Technology. 2025;13(4). (In Russ.). https://doi.org/10.26102/2310-6018/2025.51.4.064