Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
Open accessAbstract
Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consists of a recommendation module to predict preferred items for users and a conversation module to generate appropriate responses. To develop an effective CRS, it is essential to seamlessly integrate the two modules. Existing works either design semantic alignment strategies, or share knowledge resources and representations between the two modules. However, these approaches still rely on different architectures or techniques to develop the two modules, making it difficult for effective module integration. To address this problem, we propose a unified CRS model named UniCRS based on knowledge-enhanced prompt learning. Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach. In the prompt design, we include fused knowledge representations, task-specific soft tokens, and the dialogue context, which can provide sufficient contextual information to adapt the PLM for the CRS task. Besides, for the recommendation subtask, we also incorporate the generated response template as an important part of the prompt, to enhance the information interaction between the two subtasks. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://github.com/RUCAIBox/UniCRS.
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Compared to attribute-based QA methods, generative methods appear simpler and more scalable.
“Compared to attribute-based QA methods, generative methods [219-222] appear to be simpler and more scalable.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [221]
Generative methods require a large amount of supervised training data.
“However, they require a large amount of supervised training data.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [221]
Common approaches in conversational recommender systems can be divided into two categories: attribute-based QA and generative methods.
“Based on the classification of dialogue systems, common approaches in conversational recommender systems can also be divided into two categories: attribute-based QA (questionanswering) and generative methods.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [221]
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Qiu, Z., Luo, L., Pan, S., & Liew, A. W. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00159 · Cited by 10 · see citing works
A Flexible Two-Tower Model for Item Cold-Start Recommendation
Lee, W.-M., & Cho, Y.-S. (2023) · IEEE Access · doi:10.1109/access.2023.3346918 · Cited by 4 · see citing works
Preference-Conditioned Language-Guided Abstraction
Peng, A., Bobu, A., Li, B. Z., Sumers, T. R., Sucholutsky, I., Kumar, N., Griffiths, T. L., & Shah, J. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610977.3634930 · Cited by 6 · see citing works
Wang, Y., Zhou, L., Wang, Y., & Peng, Z. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/1941221 · Cited by 4 · see citing works
Qian, Z., Zhu, H., Liu, J., & Wan, Z. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3494265 · Cited by 4 · see citing works
Vague Preference Policy Learning for Conversational Recommendation
Zhang, G., Gao, C., Lei, W., Guo, X., Li, S., Chen, H., Ding, Z., Xu, S., & Wu, L. (2025) · ACM Transactions on Information Systems · doi:10.1145/3717831 · Cited by 4 · see citing works
Plot-aware transformer for recommender systems
Wang, S., Huang, Z., Zhang, B., Heng, X., Jiang, Y., & Sun, X. (2023) · Electronic Research Archive · doi:10.3934/era.2023160 · Cited by 4 · see citing works
Model Meets Knowledge: Analyzing Knowledge Types for Conversational Recommender Systems
Zhao, J., Wang, Y., Ren, Z., & Verberne, S. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748152 · Cited by 3 · see citing works
A Review of Existing Conversational Recommendation Systems
Zaidi, S., Rai, S., & Juneja, K. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489053 · Cited by 3 · see citing works
MCKP: Multi-aspect contextual knowledge-enhanced prompting for conversational recommender systems
Wang, Y., Zhang, Y., Zhu, J., Li, Y., & Zhou, W. G. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121315 · Cited by 3 · see citing works
A Unified Prompt-aware Framework for Personalized Search and Explanation Generation
Zhang, H., Zhu, Q., & Dou, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3716131 · Cited by 3 · see citing works
Generative Event Extraction via Internal Knowledge-Enhanced Prompt Learning
Song, H., Zhu, Q., Yu, Z., Liang, J., & He, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44192-9_8 · Cited by 3 · see citing works
Reinforced Target-driven Conversational Promotion
Dao, H., Liao, L., Le, D. D., & Nie, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.775 · Cited by 3 · see citing works
Semantics Driven Multi-View Knowledge Graph Embedding for Cross-Lingual Entity Alignment
Zhang, X., Liu, Y., & Zhao, Z. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446628 · Cited by 3 · see citing works
Yang, H., Kim, D., Park, G., Yeom, K., & Lee, K.-H. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3536464 · Cited by 3 · see citing works
A self-supervised entity alignment framework via attribute correction
Zhang, X., Liu, Y., Wei, H., Shan, S., & Zhao, Z. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102167 · Cited by 2 · see citing works
Sentimentally enhanced conversation recommender system
Liu, F., Cao, Q., Huang, X., & Liu, H. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01766-9 · Cited by 2 · see citing works
Multi-source information diffusion model for conversation recommender system
Liu, F., Huang, X., & Liu, H. (2025) · Evolving Systems · doi:10.1007/s12530-025-09692-y · Cited by 2 · see citing works
Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation
Jeon, H., Koide, S., Wang, Y., He, Z., & McAuley, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736828 · Cited by 2 · see citing works
STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Yang, Z., Chen, J., Li, H., Jin, X., Li, X., Zhang, J., Gao, H., Wei, K., & Wang, S. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761186 · Cited by 2 · see citing works
Haller, O., Meidan, Y., Mimran, D., Elovici, Y., & Shabtai, A. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789151 · Cited by 3 · see citing works
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
Gupta, R., Aksitov, R., Phatale, S., Chaudhary, S., Lee, H., & Rastogi, A. (2023) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2023.nlp4convai-1.13 · Cited by 3 · see citing works
Zhang, Z., Laud, T., He, Z., Chen, X., Liu, X., Xie, Z., McAuley, J., & He, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30588 · Cited by 2 · see citing works
- Authors
- Wang, X., Zhou, K., Wen, J.-R., & Zhao, W. X.
- Affiliations
- Renmin University of China (CN)
- Venue
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2022-08-14
- Pages
- 1929-1937
- OpenAlex
- W4283324387
- Cited by
- 164
- References
- 14
- Citations by year
- 2026: 32 · 2025: 56 · 2024: 49 · 2023: 26 · 2022: 1
- FWCI
- 29.51
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (BJJWZYJH012019100020098, 61872369)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- conversational recommender systems, pre-trained language models, knowledge fusion, dialogue context, user preference elicitation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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