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Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by capturing user preferences through interactive dialogues.
X. Wang, K. Zhou, J. Wen, and W. X. Zhao, “Towards unified conversational recommender systems via knowledge-enhanced prompt learning,” in Proc. of KDD , 2022, pp. 1929–1937
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K. Christakopoulou, F. Radlinski, and K. Hofmann, “Towards conversational recommender systems,” in Proc. of KDD , 2016, pp. 815–824
2016
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T. Donkers, B. Loepp, and J. Ziegler, “Sequential user-based recurrent neural network recommendations,” in Proc. of Recs , 2017, p. 152–160
2017
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R. Li, S. E. Kahou, H. Schulz, V. Michalski, L. Charlin, and C. Pal, “Towards deep conversational recommendations,” in Proc. of NeurIPS , 2018, pp. 9748–9758
2018
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Y. Sun and Y. Zhang, “Conversational recommender system,” in Proc. of ACM SIGIR , 2018, pp. 235–244
2018
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C. Chen, M. Zhang, Y. Liu, and S. Ma, “Neural attentional rating regression with review-level explanations,” in Proc. of ACM WWW , 2018, p. 1583–1592
2018
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M. Schlichtkrull, T. N. Kipf, P. Bloem, R. vanden Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in The Semantic Web , 2018, pp. 593–607
2018
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Q. Chen, J. Lin, Y. Zhang, M. Ding, Y. Cen, H. Yang, and J. Tang, “Towards knowledge-based recommender dialog system,” in Proc. of EMNLP , 2019, pp. 1803–1813
2019
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X. Wang, X. He, Y. Cao, M. Liu, and T. Chua, “KGAT: knowledge graph attention network for recommendation,” in Proc. of KDD , 2019, pp. 950–958
2019
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. of AACL , 2019, pp. 4171–4186
2019
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019
2019
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K. Zhou, W. X. Zhao, S. Bian, Y. Zhou, J. Wen, and J. Yu, “Improving conversational recommender systems via knowledge graph based semantic fusion,” in Proc. of KDD , 2020, pp. 1006–1014
2020
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Y. Zhang and X. Chen, “Explainable recommendation: A survey and new perspectives,” Foundations and Trends® in Information Retrieval , 2020
2020
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S. A. Hayati, D. Kang, Q. Zhu, W. Shi, and Z. Yu, “INSPIRED: Toward sociable recommendation dialog systems,” in Proc. of EMNLP , 2020, pp. 8142–8152
2020
Earlier work this paper cites.
D. Jannach, A. Manzoor, W. Cai, and L. Chen, “A Survey on Conversational Recommender Systems,” ACM Comput. Surv. , 2021
2021
Earlier work this paper cites.
C. Gao, W. Lei, X. He, M. de Rijke, and T.-S. Chua, “Advances and challenges in conversational recommender systems: A survey,” AI Open , vol. 2, pp. 100–126, 2021
2021
Cited alongside, same era.
Y. Lu, J. Bao, Y. Song, Z. Ma, S. Cui, Y. Wu, and X. He, “RevCore: Review-augmented conversational recommendation,” in Proc. of ACL Findings , 2021, pp. 1161–1173
2021
Cited alongside, same era.
L. Wang, H. Hu, L. Sha, C. Xu, D. Jiang, and K.-F. Wong, “RecInDial: A unified framework for conversational recommendation with pretrained language models,” in Proc. of AACL , 2022
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in Proc. of ICONIP , 2022
2022
Cited alongside, same era.
Q. Ma, X. Ren, and C. Huang, “XRec: Large language models for explainable recommendation,” in Proc. of EMNLP Findings , 2024
2024
Closest in time.
Y. Wang, C. Tian, B. Hu, Y. Yu, Z. Liu, Z. Zhang, J. Zhou, L. Pang, and X. Wang, “Can small language models be good reasoners for sequential recommendation?” in Proc. of ACM WWW , 2024, pp. 3876–3887
2024
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L. Zhu, X. Huang, and J. Sang, “How reliable is your simulator? analysis on the limitations of current llm-based user simulators for conversational recommendation,” in Proc. of ACM WWW , 2024
2024
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2024
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Y. Zhou, K. Zhou, W. X. Zhao, C. Wang, P. Jiang, and H. Hu, “C²-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System,” in Proc. of WSDM , 2022, pp. 1488–1496
2022
Cited alongside, same era.
2022
Cited alongside, same era.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” in Proc. of ICLR , 2022
2022
Cited alongside, same era.
X. Wang, X. Tang, X. Zhao, J. Wang, and J.-R. Wen, “Rethinking the evaluation for conversational recommendation in the era of large language models,” in Proc. of EMNLP , 2023
2023
Cited alongside, same era.
Z. He, Z. Xie, R. Jha, H. Steck, D. Liang, Y. Feng, B. P. Majumder, N. Kallus, and J. Mcauley, “Large language models as zero-shot conversational recommenders,” in Proc. of ACM CIKM , 2023
2023
Cited alongside, same era.
Y. Feng, S. Liu, Z. Xue, Q. Cai, L. Hu, P. Jiang, K. Gai, and F. Sun, “A large language model enhanced conversational recommender system,” 2023
2023
Cited alongside, same era.
L. Friedman et al. , “Leveraging large language models in conversational recommender systems,” 2023
2023
Cited alongside, same era.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in Proc. of NeurIPS , vol. 36, 2023, pp. 34 892–34 916
2023
Cited alongside, same era.
Y. Xi, W. Liu, J. Lin, B. Chen, R. Tang, W. Zhang, and Y. Yu, “Memocrs: Memory-enhanced sequential conversational recommender systems with large language models,” in Proc. of ACM CIKM , 2024, p. 2585–2595
2024
Closest in time.
W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, and S. Hoi, “Instructblip: towards general-purpose vision-language models with instruction tuning,” in Proc. of NeurIPS , 2024
2024
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M. Ravaut, H. Zhang, L. Xu, A. Sun, and Y. Liu, “Parameter-efficient conversational recommender system as a language processing task,” in Proc. of EACL , 2024
2024
Closest in time.
H. Dao, Y. Deng, D. D. Le, and L. Liao, “Broadening the view: Demonstration-augmented prompt learning for conversational recommendation,” in Proc. of ACM SIGIR , 2024, p. 785–795
2024
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2024
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2025
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Y. Li, X. Zhang, L. Luo, H. Chang, Y. Ren, I. King, and J. Li, “G-refer: Graph retrieval-augmented large language model for explainable recommendation,” in Proc. of ACM WWW , 2025, p. 240–251
2025
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Z. Qiu, Y. Tao, S. Pan, and A. W.-C. Liew, “Knowledge graphs and pretrained language models enhanced representation learning for conversational recommender systems,” IEEE Transactions on Neural Networks and Learning Systems , vol. 36, no. 4, pp. 6107–6121, 2025
2025
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Z. Qiu, L. Luo, Z. Zhao, S. Pan, and A. W.-C. Liew, “Graph retrieval-augmented llm for conversational recommendation systems,” in Advances in Knowledge Discovery and Data Mining . Springer Nature Singapore, 2025, pp. 344–355
2025
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Y. Wei, J. Zou, W. Guo, G. Wang, X. Xu, and Y. Yang, “Mscrs: Multi-modal semantic graph prompt learning framework for conversational recommender systems,” in Proc. of ACM SIGIR , 2025, p. 42–52
2025
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