Memory-Augmented Large Language Models for Adaptive E-Commerce Recommendation Systems
Keywords:
large language models, memory augmentation, e-commerce recommendation, retrieval-augmented generation, user modeling, system governance, fairness, sustainabilityAbstract
Large language models have introduced new opportunities for e-commerce recommendation systems by enabling semantic reasoning over user behavior, product descriptions, and interaction contexts. However, the parametric memory of static models is poorly suited to the rapidly shifting preferences and session-specific constraints that characterize real-world retail platforms. Memory-augmented large language models address this limitation by combining parametric generation with persistent user-specific memory stores, retrieval mechanisms, and adaptive update policies. This paper provides a system-level examination of memory-augmented large language models for adaptive e-commerce recommendation. It analyzes architectural foundations, memory lifecycle management, structural trade-offs between retrieval and generation, and the implications for latency, scalability, and correctness. The discussion further addresses robustness, fairness, governance, sustainability, and deployment constraints. Rather than focusing on narrow model-level metrics, the paper foregrounds the socio-technical and infrastructural dimensions that determine whether such systems can be deployed responsibly. The analysis shows that memory augmentation can improve personalization and intent alignment, but it introduces new failure modes related to stale memories, privacy leakage, feedback loops, and resource consumption. These challenges require coordinated architectural and organizational safeguards, including memory access controls, audit frameworks, lifecycle policies, and evaluation protocols that extend beyond predictive accuracy. The paper concludes by outlining open research directions and policy considerations for sustainable and fair deployment of memory-augmented recommendation systems.
References
1. Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, Y., Hu, Y., Wei, Y., Liu, Z., Chen, E. (2024). A survey on large language models for recommendation. World Wide Web, 27(5), Article 60.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
4. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
5. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, 173–182.
6. Kang, W.-C., & McAuley, J. (2018). Self-attentive sequential recommendation. Proceedings of the 2018 IEEE International Conference on Data Mining, 197–206.
7. Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don’t stop pretraining: Adapt language models to domains and tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8342–8360.
8. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), Article 5.
9. Yu, X. (2026). Enhancing Search Efficiency through LLM-Based User Memory Systems for Query Matching and Intent Modeling.
10. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
11. 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), Article 115.
12. Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., ... Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.
13. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
14. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
15. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. Proceedings of the 5th International Conference on Learning Representations.
16. Fedus, W., Zoph, B., & Shazeer, N. (2022). Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23(120), 1–39.
17. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
18. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., ... Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
19. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., ... Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
20. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin Press.
21. Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 1441–1450.
22. Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2016). Session-based recommendations with recurrent neural networks. Proceedings of the 4th International Conference on Learning Representations.
23. Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R. (2020). S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1893–1902.
24. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
25. McGuffie, K., & Newhouse, A. (2020). The radicalization risks of GPT-3 and advanced neural language models. arXiv preprint arXiv:2009.06807.
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