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A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue.
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Explore, exploit, and explain: personalizing explainable recommendations with bandits. In Proceedings of the 12th ACM conference on recommender systems . 31–39
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Personalizing dialogue agents: I have a dog, do you have pets too?
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Towards knowledge-based recommender dialog system
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Designing a conversational travel recommender system based on data-driven destination characterization. In ACM RecSys workshop on recommenders in tourism . 17–21
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Explainable recommendation through attentive multi-view learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 3622–3629
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ConveRT: Efficient and accurate conversational representations from transformers
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Recsim: A configurable simulation platform for recommender systems
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Recommendation as a communication game: Self-supervised bot-play for goal-oriented dialogue
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Hierarchical transformers for multi-document summarization
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Design considerations for explanations made by a recommender chatbot. In IASDR Conference 2019 . IASDR
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems . 269–277
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Survey on applications of multi-armed and contextual bandits. In 2020 IEEE Congress on Evolutionary Computation (CEC) . IEEE, 1–8
Learning to ask appropriate questions in conversational recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 808–817
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Prompt programming for large language models: Beyond the few-shot paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems . 1–7
Laria Reynolds and Kyle McDonell. 2021 · 2021
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Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
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You get what you chat: Using conversations to personalize search-based recommendations. In European Conference on Information Retrieval . Springer, 207–223
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Djallel Bouneffouf, Irina Rish, and Charu Aggarwal. 2020 · 2020
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TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems
Bill Byrne, Karthik Krishnamoorthi, Saravanan Ganesh, and Mihir Sanjay Kale. 2020 · 2020
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Predicting user intents and satisfaction with dialogue-based conversational recommendations. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization . 33–42
Wanling Cai and Li Chen. 2020 · 2020
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Generative adversarial networks
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INSPIRED: Toward sociable recommendation dialog systems
Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, and Zhou Yu. 2020 · 2020
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End-to-End Learning for Conversational Recommendation: A Long Way to Go?. In IntRS@ RecSys . 72–76
Dietmar Jannach and Ahtsham Manzoor. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Off-policy learning in two-stage recommender systems. In Proceedings of The Web Conference 2020 . 463–473
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Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer. 2021 · 2021
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Ethical and social risks of harm from language models
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Beyond goldfish memory: Long-term open-domain conversation
Jing Xu, Arthur Szlam, and Jason Weston. 2021 · 2021
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GPT3Mix: Leveraging large-scale language models for text augmentation
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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A Mixture-of-Expert Approach to RL-based Dialogue Management
Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, MoonKyung Ryu, Mohammad Ghavamzadeh, and Craig Boutilier. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Dialog inpainting: Turning documents into dialogs. In International Conference on Machine Learning . PMLR, 4558–4586
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Towards Reasoning in Large Language Models: A Survey
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Large language models can self-improve
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Conversational Recommendation: A Grand AI Challenge
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Survey of hallucination in natural language generation
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Can language models learn from explanations in context?
Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill. 2022 · 2022
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Lamda: Language models for dialog applications
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