extended-abstract

User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models

Author:

Gangyi ZhangAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 1349 - 1354

https://doi.org/10.1145/3604915.3608885

Published: 14 September 2023 Publication History

Abstract

Conversational recommender systems (CRS) promise to provide a more natural user experience for exploring and discovering items of interest through ongoing conversation. However, effectively modeling and adapting to users’ complex and changing preferences remains challenging. This research develops user-centric methods that focus on understanding and adapting to users throughout conversations to provide the most helpful recommendations. First, a graph-based Conversational Path Reasoning (CPR) framework is proposed that represents dialogs as interactive reasoning over a knowledge graph to capture nuanced user interests and explain recommendations. To further enhance relationship modeling, graph neural networks are incorporated for improved representation learning. Next, to address uncertainty in user needs, the Vague Preference Multi-round Conversational Recommendation (VPMCR) scenario and matching Adaptive Vague Preference Policy Learning (AVPPL) solution are presented using reinforcement learning to tailor recommendations to evolving preferences. Finally, opportunities to leverage large language models are discussed to further advance user experiences via advanced user modeling, policy learning, and response generation. Overall, this research focuses on designing conversational recommender systems that continuously understand and adapt to users’ ambiguous, complex and changing needs during natural conversations.

References

[1]

Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. arXiv preprint arXiv:2305.00447 (2023).

[2]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.

[3]

Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, and Jie Tang. 2019. Towards Knowledge-Based Recommender Dialog System. In EMNLP-IJCNLP. 1803–1813.

[4]

Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, and Lingfei Wu. 2023. Meta Policy Learning for Cold-Start Conversational Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 222–230.

Digital Library

[5]

Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, and Wai Lam. 2021. Unified conversational recommendation policy learning via graph-based reinforcement learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1431–1441.

Digital Library

[6]

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021. Advances and Challenges in Conversational Recommender Systems: A Survey. AI Open 2 (2021), 100–126. https://doi.org/10.1016/j.aiopen.2021.06.002

[7]

Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023. Chat-rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524 (2023).

[8]

Shuyu Guo, Shuo Zhang, Weiwei Sun, Pengjie Ren, Zhumin Chen, and Zhaochun Ren. 2023. Towards Explainable Conversational Recommender Systems. (2023).

[9]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639–648.

Digital Library

[10]

Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2023. Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845 (2023).

[11]

Chenhao Hu, Shuhua Huang, Yansen Zhang, and Yubao Liu. 2022. Learning to Infer User Implicit Preference in Conversational Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 256–266.

Digital Library

[12]

Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023. Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction. arXiv preprint arXiv:2305.06474 (2023).

[13]

Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation–Action–Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. In WSDM. 304–312.

[14]

Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, and Tat-Seng Chua. 2020. Interactive path reasoning on graph for conversational recommendation. In Proceedings of the 26th acm sigkdd international conference on knowledge discovery & data mining. 2073–2083.

Digital Library

[15]

Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018. Towards Deep Conversational Recommendations. In Proceedings of the 32nd International Conference on Neural Information Processing Systems(NeurIPS ’18). 9748–9758.

Digital Library

[16]

Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, and Tat-Seng Chua. 2021. Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users. ACM Trans. Inf. Syst. 39, 4, Article 40 (aug 2021), 29 pages. https://doi.org/10.1145/3446427

Digital Library

[17]

Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2018. Knowledge-aware Multimodal Dialogue Systems. In ACM MM. 801–809.

[18]

Allen Lin, Jianling Wang, Ziwei Zhu, and James Caverlee. 2022. Quantifying and mitigating popularity bias in conversational recommender systems. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1238–1247.

Digital Library

[19]

Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, and Ting Liu. 2020. Towards conversational recommendation over multi-type dialogs. arXiv preprint arXiv:2005.03954 (2020).

[20]

Yu Lu, Junwei Bao, Yan Song, Zichen Ma, Shuguang Cui, Youzheng Wu, and Xiaodong He. 2021. RevCore: Review-augmented conversational recommendation. arXiv preprint arXiv:2106.00957 (2021).

[21]

Wenchang Ma, Ryuichi Takanobu, Minghao Tu, and Minlie Huang. 2020. Bridging the gap between conversational reasoning and interactive recommendation. arXiv preprint arXiv:2010.10333 (2020).

[22]

Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. 2019. OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics(ACL ’19). 845–854.

[23]

Tianshu Shen, Jiaru Li, Mohamed Reda Bouadjenek, Zheda Mai, and Scott Sanner. 2022. Unintended Bias in Language Model-drivenConversational Recommendation. arXiv preprint arXiv:2201.06224 (2022).

[24]

Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. In SIGIR. 235–244.

[25]

Lei Wang and Ee-Peng Lim. 2023. Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. arXiv preprint arXiv:2304.03153 (2023).

[26]

Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. 2023. Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models. arXiv preprint arXiv:2305.13112 (2023).

[27]

Xiaolei Wang, Kun Zhou, Ji-Rong Wen, and Wayne Xin Zhao. 2022. Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1929–1937.

Digital Library

[28]

Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, and Tat-Seng Chua. 2022. State graph reasoning for multimodal conversational recommendation. IEEE Transactions on Multimedia (2022).

Digital Library

[29]

Kerui Xu, Jingxuan Yang, Jun Xu, Sheng Gao, Jun Guo, and Ji-Rong Wen. 2021. Adapting User Preference to Online Feedback in Multi-Round Conversational Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining(WSDM ’21). 364–372.

Digital Library

[30]

Gangyi Zhang, Chongming Gao, Wenqiang Lei, Xiaojie Guo, Shijun Li, Lingfei Wu, Hongshen Chen, Zhuozhi Ding, Sulong Xu, and Xiangnan He. 2023. Embracing Uncertainty: Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation. arXiv preprint arXiv:2306.04487 (2023).

[31]

Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. arXiv preprint arXiv:2305.07609 (2023).

[32]

Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001 (2023).

[33]

Shuo Zhang and Krisztian Balog. 2020. Evaluating conversational recommender systems via user simulation. In Proceedings of the 26th acm sigkdd international conference on knowledge discovery & data mining. 1512–1520.

Digital Library

[34]

Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W Bruce Croft. 2018. Towards conversational search and recommendation: System ask, user respond. In CIKM. 177–186.

[35]

Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, and Jian Pei. 2022. Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation. In Proceedings of the ACM Web Conference 2022. 2153–2162.

Digital Library

[36]

Jinfeng Zhou, Bo Wang, Ruifang He, and Yuexian Hou. 2021. CRFR: Improving conversational recommender systems via flexible fragments reasoning on knowledge graphs. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 4324–4334.

[37]

Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-Rong Wen, and Jingsong Yu. 2020. Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(SIGKDD’ 20). 1006–1014.

Digital Library

[38]

Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, and Ji-Rong Wen. 2020. Towards topic-guided conversational recommender system. arXiv preprint arXiv:2010.04125 (2020).

[39]

Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao, Cheng Wang, Peng Jiang, and He Hu. 2022. C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1488–1496.

Digital Library

Cited By

Kim WLim SKim GChoi S(2025)Extracting Implicit User Preferences in Conversational Recommender Systems Using Large Language ModelsMathematics10.3390/math1302022113:2(221)Online publication date: 10-Jan-2025
https://doi.org/10.3390/math13020221
Yin YPan YBao XHuang F(2025)Conversational Recommendations With User Entity Focus and Multi-Granularity Latent Variable EnhancementIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352328337:3(1126-1139)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3523283
Vančura VKordík PStraka M(2024)beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691707(1102-1107)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691707
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Index Terms

User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 14 September 2023

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Kim WLim SKim GChoi S(2025)Extracting Implicit User Preferences in Conversational Recommender Systems Using Large Language ModelsMathematics10.3390/math1302022113:2(221)Online publication date: 10-Jan-2025
https://doi.org/10.3390/math13020221
Yin YPan YBao XHuang F(2025)Conversational Recommendations With User Entity Focus and Multi-Granularity Latent Variable EnhancementIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352328337:3(1126-1139)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3523283
Vančura VKordík PStraka M(2024)beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691707(1102-1107)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691707
Maes UMichiels LSmets A(2024)GenUI(ne) CRS: UI Elements and Retrieval-Augmented Generation in Conversational Recommender Systems with LLMsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691697(1177-1179)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691697
Zhang XXie RLyu YXin XRen PLiang MZhang BKang Zde Rijke MRen Z(2024)Towards Empathetic Conversational Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688133(84-93)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688133
Zhang GGao CPan HTeng RLi RSerra ESpezzano F(2024)Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679792(3135-3144)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679792
Yang DChen FFang HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657924(2286-2290)Online publication date: 10-Jul-2024
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Wang JKaratzoglou AArapakis IJose JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action ModelingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657767(375-385)Online publication date: 10-Jul-2024
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Dai XWang ZXie JLiu XLui J(2024)Conversational Recommendation With Online Learning and Clustering on Misspecified UsersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342344236:12(7825-7838)Online publication date: Dec-2024
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Shu YZhang HGu HZhang PLu TLi DGu N(2024)RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM AgentsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340403911:5(6759-6770)Online publication date: Oct-2024
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