Adaptive E-Commerce Search Optimization Using Large Language Models and Dynamic User Interest Memory Networks
Keywords:
e-commerce search, large language models, user memory networks, personalization, information retrieval, fairness, system architectureAbstract
The rapid growth of e-commerce platforms has made product search a critical interface between consumers and vast digital catalogs, yet traditional retrieval and ranking pipelines struggle to capture the fluid and context-dependent nature of user intent. This paper presents a comprehensive system-level investigation of adaptive e-commerce search optimization that integrates large language models with dynamic user interest memory networks. We examine the architectural fusion of transformer-based language understanding with dynamically updating memory components that continuously encode short-term and long-term user preferences, session context, and cross-session behavioral signals. The discussion is organized around structural trade-offs, infrastructure design, deployment considerations, and governance challenges, including fairness, robustness, privacy, and sustainability. By situating the proposed approach within the broader evolution from feature-based learning to rank and neural retrieval models, we analyze how memory-augmented large language models can enable more coherent and personalized query matching and intent modeling. The paper does not present a single system implementation but instead develops a holistic research perspective that connects advances in dense retrieval, interest evolution networks, and zero-shot ranking capabilities with the systemic demands of production-scale e-commerce environments. Throughout, we emphasize the interplay between model expressiveness and operational constraints, the necessity of continuous model updating to combat concept drift, the policy implications of profiling user memory, and the environmental costs of serving large generative models at scale. The analysis is grounded in recent literature and provides forward-looking perspectives on building inclusive, privacy-preserving, and energy-efficient search systems that respect user autonomy while delivering high-quality discovery experiences.
References
1. Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), 225-331.
2. Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017). Neural ranking models with weak supervision. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 65-74).
3. Guo, J., Fan, Y., Ai, Q., & Croft, W. B. (2020). A deep look into neural ranking models for information retrieval. Information Processing & Management, 57(6), 102070.
4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998-6008).
5. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171-4186).
6. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (pp. 1877-1901).
7. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), 1-38.
8. Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., ... & Gai, K. (2018). Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1059-1068).
9. Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., ... & Gai, K. (2019). Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5941-5948.
10. Pi, Q., Bian, W., Zhou, G., Zhu, X., & Gai, K. (2019). Practice on long sequential user behavior modeling for click-through rate prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2671-2679).
11. Reddy, C. K., Marquez, L., Valero, F., Rao, N., Zaragoza, H., Bandyopadhyay, S., ... & Kanter, J. (2019). Shopping queries dataset: A large-scale e-commerce search relevance dataset. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1397-1400).
12. Yu, X. (2026). Enhancing Search Efficiency through LLM-Based User Memory Systems for Query Matching and Intent Modeling.
13. Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 6769-6781).
14. Sukhbaatar, S., Szlam, A., Weston, J., & Fergus, R. (2015). End-to-end memory networks. In Advances in Neural Information Processing Systems (pp. 2440-2448).
15. 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.
16. Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In Proceedings of the 2008 IEEE Symposium on Security and Privacy (pp. 111-125).
17. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623).
18. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
19. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), 225-331.
20. Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017). Neural ranking models with weak supervision. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 65-74).
21. Guo, J., Fan, Y., Ai, Q., & Croft, W. B. (2020). A deep look into neural ranking models for information retrieval. Information Processing & Management, 57(6), 102070.
22. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998-6008).
23. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171-4186).
24. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (pp. 1877-1901).
25. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), 1-38.
26. Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., ... & Gai, K. (2018). Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1059-1068).
27. Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., ... & Gai, K. (2019). Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5941-5948.
28. Pi, Q., Bian, W., Zhou, G., Zhu, X., & Gai, K. (2019). Practice on long sequential user behavior modeling for click-through rate prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2671-2679).
29. Reddy, C. K., Marquez, L., Valero, F., Rao, N., Zaragoza, H., Bandyopadhyay, S., ... & Kanter, J. (2019). Shopping queries dataset: A large-scale e-commerce search relevance dataset. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1397-1400).
30. Yu, X. (2026). Enhancing search efficiency through LLM-based user memory systems for query matching and intent modeling. arXiv preprint arXiv:2601.01234.
31. Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 6769-6781).
32. Sukhbaatar, S., Szlam, A., Weston, J., & Fergus, R. (2015). End-to-end memory networks. In Advances in Neural Information Processing Systems (pp. 2440-2448).
33. 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.
34. Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In Proceedings of the 2008 IEEE Symposium on Security and Privacy (pp. 111-125).
35. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623).
36. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
37. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
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