Learning-to-Rank in B2B e-commerce catalogs: A digital exper-iment and conversion analysis
Abstract
Amid intensifying competition in the B2B e-commerce sector, particularly within the Do-It-Yourself (DIY) segment, traditional static search architectures increasingly suffer from limited adaptability and declining retrieval relevance. This study examines the limitations of rule-based ranking approaches and proposes a dynamic product ranking framework based on the Learning-to-Rank paradigm implemented with LightGBM. The primary objective of the research is to quantitatively evaluate the economic return on investment (ROI) associated with the deployment of personalized ranking algorithms. A simulation-based digital experiment was conducted using a synthetic user clickstream model to approximate real-world interaction behavior. The results indicate that the proposed dynamic ranking model yields significant improvements in search effectiveness, as measured by the metric, while simultaneously generating quantifiable gains in key business performance indicators. Specifically, the implementation resulted in a 2.1 percentage point increase in the conversion rate and a 14.5% uplift in incremental revenue. These observed effects achieved statistical significance. These findings provide empirical evidence supporting the economic viability of transitioning from static search systems to intelligent ranking architectures, highlighting their strategic importance for scalable and competitive B2B e-commerce platforms.
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References
Kuznetsov, A. V. (2022). Ekonomika tsifrovykh platform [Economics of digital platforms]. Moscow: HSE Publishing House (in Russian).
Guevara, Y. E. (2009). The Home Depot paradox: An investigation of the relationship between financial performance and customer satisfaction (PhD Thesis). Capella University.
Chen, L., & Xiong, R. (2016). Predicting the relevance of search results on Homedepot.com. Project report. http://cs224d.stanford.edu/reports/ChenXiong.pdf
Tool-kit.tech. (2025). Petrovich. Supplier's personal account (in Russian). https://ruward.ru/award/2025/571712/
ETM Company. (2024). iPRO Online Service (in Russian). https://www.etm.ru/
Burges, C. J. C. (2005). Learning to rank using gradient descent. Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 89–96. https://doi.org/10.1145/1102351.1102363
Joachims, T. (2002). Optimizing search engines using clickthrough data. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), 133–142. https://doi.org/10.1145/775047.775067
Li, H. (2014). Learning to rank for information retrieval and natural language processing. Foundations and Trends in Information Retrieval, 8(1–2), 1–190.
Li, S., Lv, F., Jin, T., Lin, G., Yang, K., Zeng, X., Wu, X.-M., & Ma, Q. (2021). Embedding-based product retrieval in Taobao search. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 3181–3189. https://doi.org/10.1145/3447548.3467101
Rahman, M. D. S. (2024). E-commerce evolution: A strategic analysis of Alibaba’s business ecosystem. SSRN. https://doi.org/10.2139/ssrn.5109165
Dixit, P. (2024). Competitive analysis of global e-commerce platforms: A Comparative study of Amazon, Alibaba, and Ebay. Educational Administration: Theory and Practice, 30(1), 5628–5635.
Lee, J., Bernier-Colborne, G., Maharaj, T., & Vajjala, S. (2024). Methods, applications, and directions of Learning-to-Rank in NLP research. Findings of the Association for Computational Linguistics: NAACL 2024, 1900–1917. https://doi.org/10.18653/v1/2024.findings-naacl.123
Hofstätter, S., Lin, S.-C., Yang, J.-H., Lin, J., & Hanbury, A. (2021). Efficiently teaching an effective dense retriever with balanced topic aware sampling. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 113–122. https://doi.org/10.1145/3404835.3462891
Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., & Gurevych, I. (2021). BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. arXiv:2104.08663. https://doi.org/10.48550/arXiv.2104.08663
Zamani, H., Diaz, F., Dehghani, M., Metzler, D., & Bendersky, M. (2022). Retrieval-enhanced machine learning. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2875–2886. https://doi.org/10.1145/3477495.3531722
MacAvaney, S., Nardini, F. M., Perego, R., Tonellotto, N., Goharian, N., & Frieder, O. (2020). Efficient document re-ranking for transformers by precomputing term representations. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 49–58. https://doi.org/10.1145/3397271.3401093
Chapelle, O., & Zhang, Y. (2009). A dynamic bayesian network click model for web search ranking. Proceedings of the 18th International Conference on World Wide Web (WWW '09), 1–10. https://doi.org/10.1145/1526709.1526711
Trzebiński, W., & Marciniak, B. (2023). Meaning or Importance? E-commerce consumers interest in product features presented in online offerings: The role of self-relevance and information processing. Journal of Internet Commerce, 22(2), 224–243. https://doi.org/10.1080/15332861.2022.2042116
Bigdeli, A., Arabzadeh, N., Bagheri, E., & Clarke, C. L. A. (2024). Evaluating relative retrieval effectiveness with normalized residual gain. Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 64–71. https://doi.org/10.1145/3673791.3698410
Ara, J., Ghodke, S., Akter, J., & Roy, A. (2025). Optimizing e-commerce platforms with AI-enabled visual search: Assessing user behavior, interaction metrics, and system accuracy. Journal of Economics, Finance and Accounting Studies, 7(3), 09-17. https://doi.org/10.32996/jefas.2025.7.3.2
Ke, G., Meng, Q., Finely, T., Wang, T., Chen, W., Ma, W., & Ye, Q. (2017). LightGBM: A highly efficient gradient boosting decision tree. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 3146–3154. https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf
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