Conversational Recommender System
Open accessAbstract
A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business oriented metrics such as conversion rate or expected revenue. In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function. In particular, we propose to represent a user conversation history as a semi-structured user query with facet-value pairs. This query is generated and updated by belief tracker that analyzes natural language utterances of user at each step. We propose a set of machine actions tailored for recommendation agents and train a deep policy network to decide which action (i.e. asking for the value of a facet or making a recommendation) the agent should take at each step. We train a personalized recommendation model that uses both the user's past ratings and user query collected in the current conversational session when making rating predictions and generating recommendations. Such a conversational system often tries to collect user preferences by asking questions. Once enough user preference is collected, it makes personalized recommendations to the user. We perform both simulation experiments and real online user studies to demonstrate the effectiveness of the proposed framework.
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Existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.
“We found that existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [57]
Conversational recommender systems engage users in interactive conversations to delve deeper into preferences and fine-tune recommendations.
“Conversational Recommender Systems [24, 25] mark a remarkable stride forward in the realm of personalized assistance. By engaging users in interactive conversations, these systems delve deeper into their preferences and fine-tune their recommendations accordingly.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [24]
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 [24]
Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R. M., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,232 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,821 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works
Probabilistic Matrix Factorization
Mnih, A., & Salakhutdinov, R. (2007) · neural information processing systems · Cited by 3,538 · see citing works
Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189 · see citing works
Content-based Recommender Systems: State of the Art and Trends
Lops, P., de Gemmis, M., & Semeraro, G. M. (2010) · Springer eBooks · doi:10.1007/978-0-387-85820-3_3 · Cited by 1,941 · see citing works
An experimental comparison of click position-bias models
Craswell, N., Zoeter, O. R., Taylor, M. J. H., & Ramsey, B. (2008) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1341531.1341545 · Cited by 947 · see citing works
POMDP-Based Statistical Spoken Dialog Systems: A Review
Young, S. J., Gašić, M., Thomson, B., & Williams, J. D. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2225812 · Cited by 898 · see citing works
Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
Mesnil, G., Dauphin, Y., Yao, K., Bengio, Y., Deng, L., Hakkani-Tür, D. Z., He, X., Heck, L. P., Tür, G., Yu, D., et al. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2383614 · Cited by 461 · see citing works
Towards Conversational Recommender Systems
Christakopoulou, K., Radlinski, F., & Hofmann, K. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939746 · Cited by 421 · see citing works
The Dialog State Tracking Challenge
Williams, J. D., Raux, A., Ramachandran, D., & Black, A. W. (2013) · Cited by 315 · see citing works
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Dhingra, B., Li, L. C., Li, X., Gao, J., Chen, Y.-N., Ahmed, F., & Deng, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1045 · Cited by 274 · see citing works
Real-Time Bidding by Reinforcement Learning in Display Advertising
Cai, H., Ren, K., Zhang, W., Malialis, K., Wang, J., Yu, Y., & Guo, D. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018702 · Cited by 196 · see citing works
Personalized interactive faceted search
Koren, J., Zhang, Y., & Liu, X. (2008) · International World Wide Web Conference (WWW) · doi:10.1145/1367497.1367562 · Cited by 149 · see citing works
Self-citationReal-Time Bidding by Reinforcement Learning in Display Advertising
Cai, H., Ren, K., Zhag, W., Malialis, K., Wang, J., Yu, Y., & Guo, D. (2017) · UCL Discovery (University College London) · Cited by 115 · see citing works
Introduction to Information Retrieval
Kraft, D. H., & Colvin, E. (2017) · Synthesis lectures on information concepts, retrieval, and services · doi:10.1007/978-3-031-02307-1_1 · Cited by 85 · see citing works
Interactive retrieval based on faceted feedback
Zhang, L., & Zhang, Y. (2010) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1835449.1835511 · Cited by 41 · see citing works
Self-citationDynamic Facet Ordering for Faceted Product Search Engines
Vandić, D., Aanen, S. S., Frăsincar, F., & Kaymak, U. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2652461 · Cited by 35 · see citing works
Wu, J. Y., Li, M., & Lee, C. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2462712 · Cited by 23 · see citing works
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue SystemsPreprint
Li, X., Chen, Y.-N., Li, L. C., Gao, J., & Çelikyılmaz, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07055 · Cited by 22 · see citing works
N-best error simulation for training spoken dialogue systems
Thomson, B., Gašić, M., Henderson, M., Tsiakoulis, P., & Young, S. J. (2012) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2012.6424194 · Cited by 19 · see citing works
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2 of the 27 references couldn’t be matched to a known paper.
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A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., et al. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3568022 · Cited by 854 · see citing works
Reinforcement Learning based Recommender Systems: A Survey
Afsar, M. M., Crump, T., & Far, B. H. (2022) · ACM Computing Surveys · doi:10.1145/3543846 · Cited by 560 · see citing works
When large language models meet personalization: perspectives of challenges and opportunities
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01276-1 · Cited by 367 · see citing works
“The attribute-based QA approach [24, 216–218] utilizes a pipeline method within the dialogue system.”
InfluentialAdvances and challenges in conversational recommender systems: A survey
Gao, C., Lei, W., He, X., de Rijke, M., & Chua, T. (2021) · AI Open · doi:10.1016/j.aiopen.2021.06.002 · Cited by 360 · see citing works
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
Aliannejadi, M., Zamani, H., Crestani, F., & Croft, W. B. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331265 · Cited by 317 · see citing works
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
Moon, S., Shah, P., Kumar, A., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1081 · Cited by 306 · see citing works
Lei, W., He, X., Miao, Y., Wu, Q., Hong, R., Kan, M.-Y., & Chua, T.-S. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining · doi:10.1145/3336191.3371769 · Cited by 253 · see citing works
Towards Knowledge-Based Recommender Dialog System
Chen, Q., Lin, J., Zhang, Y., Ding, M., Cen, Y., Yang, H., & Tang, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1189 · Cited by 281 · see citing works
Self-citationHow Can Recommender Systems Benefit from Large Language Models: A Survey
Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3678004 · Cited by 242 · see citing works
Generating Clarifying Questions for Information Retrieval
Zamani, H., Dumais, S., Craswell, N., Bennett, P. N., & Lueck, G. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380126 · Cited by 222 · see citing works
Gil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2024) · Political Communication · doi:10.1080/10584609.2023.2290497 · Cited by 198 · see citing works
Towards Conversational Recommendation over Multi-Type Dialogs
Liu, Z., Wang, H., Niu, Z.-Y., Wu, H. R., Che, W., & Liu, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.98 · Cited by 180 · see citing works
Gao, C., Li, S., Lei, W., Chen, J., Li, B., Jiang, P., He, X., Mao, J., & Chua, T. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557220 · Cited by 168 · see citing works
Towards Topic-Guided Conversational Recommender System
Zhou, K., Zhou, Y., Zhao, W. X., Wang, X., & Wen, J.-R. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.365 · Cited by 190 · see citing works
Conversational agents in business: A systematic literature review and future research directions
Bavaresco, R., Silveira, D., Reis, E., Barbosa, J., Righi, R., Costa, C., Antunes, R., Gomes, M., Gatti, C., Vanzin, M., et al. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100239 · Cited by 143 · see citing works
AI-based conversational agents: A scoping review from technologies to future directions
Kusal, S., Patil, S., Choudrie, J., Kotecha, K., Mishra, S., & Abraham, A. (2022) · IEEE Access · doi:10.1109/access.2022.3201144 · Cited by 140 · see citing works
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
Deng, Y., Li, Y., Sun, F., Ding, B., & Lam, W. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462913 · Cited by 136 · see citing works
INSPIRED: Toward Sociable Recommendation Dialog Systems
Hayati, S. A., Kang, D., Zhu, Q., Shi, W., & Yu, Z. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.654 · Cited by 137 · see citing works
Contrastive Self-supervised Learning in Recommender Systems: A Survey
Jing, M., Zhu, Y., Zang, T., & Wang, K. (2023) · ACM Transactions on Information Systems · doi:10.1145/3627158 · Cited by 132 · see citing works
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users
Li, S., Lei, W., Wu, Q., He, X., Jiang, P., & Chua, T. (2021) · ACM Transactions on Information Systems · doi:10.1145/3446427 · Cited by 113 · see citing works
Kusal, S., Patil, S., Choudrie, J., Kotecha, K. V., Vora, D. R., & Pappas, I. O. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10509-0 · Cited by 159 · see citing works
A Survey on Reinforcement Learning for Recommender Systems
Lin, Y., Liu, Y., Lin, F., Zou, L., Wu, P., Zeng, W., Chen, H., & Miao, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3280161 · Cited by 112 · see citing works
Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences
Radlinski, F., Balog, K., Byrne, B., & Krishnamoorthi, K. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5941 · Cited by 92 · see citing works
Conversational Information Seeking
Zamani, H., Trippas, J. R., Dalton, J., & Radlinski, F. (2023) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000081 · Cited by 124 · see citing works
A Review on Question Generation from Natural Language Text
Zhang, R., Guo, J., Chen, L., Fan, Y., & Cheng, X. (2021) · ACM Transactions on Information Systems · doi:10.1145/3468889 · Cited by 91 · see citing works
AIR: Attentional Intention-Aware Recommender Systems
Chen, T., Yin, H., Chen, H., Yan, R., Nguyen, Q. V. H., & Li, X. (2019) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00035 · Cited by 86 · see citing works
Analyzing and Learning from User Interactions for Search Clarification
Zamani, H., Mitra, B., Chen, E., Lueck, G., Díaz, F., Bennett, P. N., Craswell, N., & Dumais, S. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401160 · Cited by 86 · see citing works
Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses
Ramesh, A., & Chawla, V. (2022) · Journal of Interactive Marketing · doi:10.1177/10949968221095549 · Cited by 91 · see citing works
RecSim: A Configurable Simulation Platform for Recommender SystemsPreprint
Ie, E., Hsu, C., Mladenov, M., Jain, V., Narvekar, S., Wang, J., Wu, R., & Boutilier, C. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.04847 · Cited by 81 · see citing works
Automated vs. Human Health Coaching
Mitchell, E. G., Maimone, R., Cassells, A. N., Tobin, J. N., Davidson, P., Smaldone, A. M., & Mamykina, L. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449173 · Cited by 78 · see citing works
Location-based Orientation Context Dependent Recommender System for Users
Joe, C. V., & Raj, J. S. (2021) · Journal of Trends in Computer Science and Smart Technology · doi:10.36548/jtcsst.2021.1.002 · Cited by 78 · see citing works
RevCore: Review-Augmented Conversational Recommendation
Yu, L., Bao, J., Song, Y., Ma, Z., Cui, S. R., Wu, Y., & He, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.99 · Cited by 92 · see citing works
CHAT-Bot: A cultural heritage aware teller-bot for supporting touristic experiences
Casillo, M., Clarizia, F., D’Aniello, G., Santo, M. D., Lombardi, M., & Santaniello, D. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.01.003 · Cited by 75 · see citing works
Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation
Xu, K., Yang, J., Xu, J., Gao, S., Guo, J. H., & Wen, J.-R. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441791 · Cited by 74 · see citing works
Conversational Recommendation: Formulation, Methods, and Evaluation
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MMConv: An Environment for Multimodal Conversational Search across Multiple Domains
Liao, L., Long, L. H., Zhang, Z., Huang, M., & Chua, T. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462970 · Cited by 69 · see citing works
The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research
Guruge, D. B., Kadel, R., & Halder, S. J. (2021) · Data · doi:10.3390/data6020018 · Cited by 69 · see citing works
Towards responsible media recommendation
Elahi, M., Jannach, D., Skjærven, L., Knudsen, E., Sjøvaag, H., Tolonen, K., Holmstad, Ø., Pipkin, I., Throndsen, E., Stenbom, A., et al. (2021) · AI and Ethics · doi:10.1007/s43681-021-00107-7 · Cited by 69 · see citing works
Chatbot design approaches for fashion E-commerce: an interdisciplinary review
Landim, A. R. D. B., Pereira, A. M., Vieira, T. M. D. A., Costa, E. D. B., Moura, J. A. B., Wanick, V., & Bazaki, E. (2021) · International Journal of Fashion Design Technology and Education · doi:10.1080/17543266.2021.1990417 · Cited by 77 · see citing works
Nie, L., Wang, W., Hong, R., Wang, M., & Tian, Q. C. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350923 · Cited by 67 · see citing works
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Wang, L., Zhang, J., Yang, H., Chen, Z.-Y., Tang, J., Zhang, Z., Chen, X., Lin, Y., Sun, H., Song, R., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3708985 · Cited by 59 · see citing works
Gheewala, S., Xu, S., & Yeom, S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10866-z · Cited by 58 · see citing works
Conversational Product Search Based on Negative Feedback
Bi, K., Ai, Q., Zhang, Y., & Croft, W. B. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357939 · Cited by 58 · see citing works
Self-citationImpacts of Personal Characteristics on User Trust in Conversational Recommender Systems
Cai, W., Jin, Y., & Chen, L. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517471 · Cited by 59 · see citing works
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Cai, W., & Chen, L. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3340631.3394856 · Cited by 56 · see citing works
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Cui, C., Wang, W., Song, X., Huang, M., Xu, X.-S., & Nie, L. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331226 · Cited by 55 · see citing works
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Ren, X., Yin, H., Chen, T., Wang, H., Nguyen, Q. V. H., Huang, Z. H., & Zhang, X. (2020) · ACM Transactions on Information Systems · doi:10.1145/3394592 · Cited by 54 · see citing works
Nie, L., Jiao, F., Wang, W., Wang, Y., & Tian, Q. C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3108724 · Cited by 52 · see citing works
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Fu, Z., Xian, Y., Zhang, Y., & Zhang, Y. (2020) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3383313.3411548 · Cited by 46 · see citing works
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Zhang, X., Xin, X., Li, D., Liu, W., Ren, P., Chen, Z., Ma, J., & Ren, Z. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570426 · Cited by 45 · see citing works
Zhou, J., Wang, B., He, R., & Hou, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.355 · Cited by 44 · see citing works
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Deng, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3174197 · Cited by 47 · see citing works
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Aliannejadi, M., Chakraborty, M., Ríssola, E. A., & Crestani, F. (2020) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3343413.3377968 · Cited by 42 · see citing works
Shen, T., Li, J., Bouadjenek, M. R., Mai, Z., & Sanner, S. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103139 · Cited by 49 · see citing works
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Xie, Z., Yu, T., Zhao, C., & Li, S. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462920 · Cited by 42 · see citing works
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Liu, Z., Wang, H., Niu, Z.-Y., Wu, H. R., & Che, W. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.356 · Cited by 41 · see citing works
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Ren, X., Chen, T., Nguyen, Q. V. H., Cui, L., Huang, Z. H., & Yin, H. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3637216 · Cited by 40 · see citing works
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Clarizia, F., Colace, F., Santo, M. D., Lombardi, M., Pascale, F., & Santaniello, D. (2019) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2019.00063 · Cited by 39 · see citing works
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Priyogi, B. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291604 · Cited by 37 · see citing works
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Xu, H., Moon, S., Liu, H., Liu, B., Shah, P., Liu, B., & Yu, P. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.463 · Cited by 39 · see citing works
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Lin, Y., Lin, F., Zeng, W., Xiahou, J., Li, L., Wu, P., Liu, Y., & Miao, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108546 · Cited by 36 · see citing works
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Liao, Q. V., Geyer, W., Muller, M. J., & Khazaen, Y. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-38825-6_13 · Cited by 37 · see citing works
Hernandez-Bocanegra, D. C., & Ziegler, J. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579541 · Cited by 33 · see citing works
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Zhang, H., Liu, M., Gao, Z., Lei, X., Wang, Y., & Nie, L. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475234 · Cited by 33 · see citing works
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Baughan, A., Wang, X., Liu, A., Mercurio, A., Chen, J., & Ma, X. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581152 · Cited by 32 · see citing works
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Rafailidis, D., & Manolopoulos, Y. (2019) · International Conference on Web Intelligence, Mining and Semantics · doi:10.1145/3326467.3326468 · Cited by 32 · see citing works
Argumentation as a Framework for Interactive Explanations for Recommendations
Rago, A., Cocarascu, O., Bechlivanidis, C., & Toni, F. (2020) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2020/83 · Cited by 33 · see citing works
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Ren, Z., Tian, Z., Li, D., Ren, P., Yang, L. Z., Xin, X., Liang, H., de Rijke, M., & Chen, Z. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532077 · Cited by 32 · see citing works
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Jin, Y., Chen, L., Cai, W., & Zhao, X. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3631534 · Cited by 33 · see citing works
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Liao, L., Takanobu, R., Ma, Y., Yang, X., Huang, M., & Chua, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00710 · Cited by 31 · see citing works
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Yu, T., Shen, Y., Zhang, R., Zeng, X., & Jin, H. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350935 · Cited by 30 · see citing works
Studying the Effectiveness of Conversational Search Refinement Through User Simulation
Salle, A., Malmasi, S., Rokhlenko, O., & Agichtein, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_39 · Cited by 31 · see citing works
Yang, Y.-C., Chen, C.-T., Lu, T.-Y., & Huang, S.-H. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3269396 · Cited by 29 · see citing works
HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation
Fu, Z., Xian, Y., Zhu, Y., Xu, S., Li, Z., de Melo, G., & Zhang, Y. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463247 · Cited by 31 · see citing works
Self-citationDifferentially private recommender system with variational autoencoders
Fang, L., Du, B., & Wu, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109044 · Cited by 29 · see citing works
Investigating meta-intents: user interaction preferences in conversational recommender systems
Ma, Y., & Ziegler, J. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09411-3 · Cited by 28 · see citing works
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals
Zhang, J., Yang, Y. T., Chen, C., He, L. J., & Yu, Z. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.94 · Cited by 28 · see citing works
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Radlinski, F., Balog, K., Díaz, F., Dixon, L., & Wedin, B. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531873 · Cited by 30 · see citing works
Qiu, Z., Tao, Y., Pan, S., & Liew, A. W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3395334 · Cited by 29 · see citing works
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Aguilar, D. C., Salamó, M., & Boratto, L. (2021) · ACM Transactions on Information Systems · doi:10.1145/3462759 · Cited by 27 · see citing works
Wang, L., Joty, S., Gao, W. G., Zeng, X., & Wong, K. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3397321 · Cited by 29 · see citing works
Deep Natural Language Processing for Search and Recommender Systems
Guo, W., Gao, H., Shi, J., Long, B., Zhang, L., Chen, B.-C., & Agarwal, D. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3332290 · Cited by 27 · see citing works
Ren, P., Liu, Z., Song, X., Tian, H., Chen, Z., Ren, Z., & de Rijke, M. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462897 · Cited by 26 · see citing works
- Authors
- Sun, Y., & Zhang, Y.
- Affiliations
- University of California, Santa Cruz (US)
- ORCID
- Zhang, Yi
- Venue
- The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
- Publisher
- ACM
- Published
- 2018-06-27
- Pages
- 235-244
- OpenAlex
- W2964112275
- Cited by
- 378
- References
- 27
- Citations by year
- 2026: 16 · 2025: 40 · 2024: 50 · 2023: 67 · 2022: 61 · 2021: 69 · 2020: 48 · 2019: 25 · 2018: 2
- FWCI
- 28.25
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- conversational recommender systems, personalized recommendation, deep reinforcement learning, dialogue systems, salespeople, e-commerce, long-term user preferences, belief tracking, rating prediction, conversion optimization
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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