Personalized Retrieval-Augmented Generation for User-Aware Conversational Recommendation Systems
Keywords:
personalized retrieval, retrieval-augmented generation, conversational recommendation, user-aware systems, socio-technical infrastructure, fairness and governance, approximate nearest neighbor search, sustainable deploymentAbstract
Personalized retrieval-augmented generation has emerged as a promising paradigm for conversational recommendation systems, yet its system-level implications remain underexamined. This paper presents a critical and architectural analysis of user-aware retrieval-augmented generation, focusing on how personalization layers interact with retrieval infrastructure, ranking mechanisms, governance frameworks, and long-term sustainability. The central argument is that effective personalization in conversational recommendation is not merely a modeling problem but a socio-technical design challenge that requires careful balancing of relevance, fairness, robustness, latency, and privacy. The paper first reviews foundations of retrieval-augmented generation and conversational recommendation, then develops a layered architecture that separates global retrieval, user-conditioned re-ranking, and dialogue-state fusion. It examines indexing and deployment trade-offs, including approximate nearest neighbor search, sharding strategies, and hardware acceleration, and discusses how these choices affect responsiveness and operational cost. The analysis then turns to governance, showing how user-aware retrieval can amplify feedback loops, filter bubbles, and privacy risks, and how policy mechanisms such as data minimization, auditability, and contestability must be embedded into system design. Robustness and evaluation are addressed through the lens of distribution shift, adversarial manipulation, and multi-objective assessment. Case illustrations from e-commerce, media streaming, and public service navigation demonstrate cross-domain variation in requirements and constraints. The paper concludes with forward-looking perspectives on federated personalization, adaptive retrieval control, and sustainable infrastructure, arguing that future progress depends on integrating technical, organizational, and policy considerations into a coherent system-level agenda.
References
1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W. T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
2. Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M. (2020). REALM: Retrieval-augmented language model pre-training. In Proceedings of the 37th International Conference on Machine Learning (pp. 3929-3938). PMLR.
3. Karpukhin, V., Oğuz, 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 (pp. 6769-6781). Association for Computational Linguistics.
4. Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 874-880). Association for Computational Linguistics.
5. Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., van den Driessche, G., Lespiau, J. B., Damoc, B., Clark, A., de Las Casas, D., Guy, A., Menick, J., Ring, R., Hennigan, T., Huang, S., Maggiore, L., Jones, C., Cassirer, A., ... & Sifre, L. (2022). Improving language models by retrieving from trillions of tokens. In Proceedings of the 39th International Conference on Machine Learning (pp. 2206-2240). PMLR.
6. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
7. Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2024). Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In Proceedings of the 12th International Conference on Learning Representations. OpenReview.
8. Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023). Active retrieval augmented generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 7969-7992). Association for Computational Linguistics.
9. Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Liang, P., & Yih, W. T. (2023). REPLUG: Retrieval-augmented black-box language models. arXiv preprint arXiv:2301.12652.
10. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (pp. 3982-3992). Association for Computational Linguistics.
11. Malkov, Y. A., & Yashunin, D. A. (2020). Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4), 824-836.
12. Johnson, J., Douze, M., & Jégou, H. (2019). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535-547.
13. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
14. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (pp. 173-182). Association for Computing Machinery.
15. Kang, W. C., & McAuley, J. (2018). Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE International Conference on Data Mining (pp. 197-206). IEEE.
16. Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1441-1450). Association for Computing Machinery.
17. Chen, Q., Zhao, H., Li, W., Huang, P., & Ou, W. (2019). Behavior sequence transformer for e-commerce recommendation in Alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (pp. 1-4). Association for Computing Machinery.
18. Deng, Q., Liu, S., Qiu, H., Long, N., Lin, W., & Chen, S. (2026, April). Unified Semantic Space and Attention-Based Fusion Framework for Multi-Source Heterogeneous Retrieval-Augmented Generation. In 2026 6th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) (pp. 591-597). IEEE.
19. Wu, L., He, X., Wang, X., Zhang, K., & Wang, M. (2022). A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering, 35(5), 4425-4445.
20. Jannach, D., Manzoor, A., Cai, W., & Chen, L. (2021). A survey on conversational recommender systems. ACM Computing Surveys, 54(5), 1-36.
21. 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). Association for Computing Machinery.
22. 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.
23. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645-3650). Association for Computational Linguistics.
24. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54-63.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Computer Science and Engineering Transactions
This work is licensed under a Creative Commons Attribution 4.0 International License.