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Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top item preferences in a cold-start setting is a key challenge for building effective and personalized conversational recommendation (ConvRec) systems.
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
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A POMDP formulation of preference elicitation problems. In AAAI/IAAI . Edmonton, AB, 239–246
Craig Boutilier. 2002 · 2002
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Acquiring both constraint and solution preferences in interactive constraint systems
Francesca Rossi and Allesandro Sperduti. 2004 · 2004
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The PASCAL recognising textual entailment challenge. In Machine Learning Challenges Workshop . Springer, 177–190
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Active preference learning with discrete choice data
Brochu Eric, Nando Freitas, and Abhijeet Ghosh. 2007 · 2007
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A Bayesian interactive optimization approach to procedural animation design. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation . 103–112
Eric Brochu, Tyson Brochu, and Nando De Freitas. 2010 · 2010
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Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics . JMLR Workshop and Conference Proceedings, 289–296
Shengbo Guo and Scott Sanner. 2010 · 2010
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Gaussian process preference elicitation
Shengbo Guo, Scott Sanner, and Edwin V Bonilla. 2010 · 2010
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A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th International Conference on World Wide Web . 661–670
Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010 · 2010
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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining . 297–306
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Interactive collaborative filtering. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management . 1411–1420
Xiaoxue Zhao, Weinan Zhang, and Jun Wang. 2013 · 2013
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Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas. 2016 · 2015
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Towards Conversational Recommender Systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16) . Association for Computing Machinery, New York, NY, USA, 815–824
Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016 · 2016
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Designing Engaging Games Using Bayesian Optimization. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16) . Association for Computing Machinery, New York, NY, USA, 5571–5582
Mohammad M. Khajah, Brett D. Roads, Robert V. Lindsey, Yun-En Liu, and Michael C. Mozer. 2016 · 2016
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Preferential bayesian optimization. In International Conference on Machine Learning . PMLR, 1282–1291
Javier González, Zhenwen Dai, Andreas Damianou, and Neil D Lawrence. 2017 · 2017
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Item recommendation with variational autoencoders and heterogeneous priors. In Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems . 10–14
Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, and Tony Jebara. 2018 · 2018
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Towards deep conversational recommendations
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018 · 2018
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FEVER: a Large-scale Dataset for Fact Extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , Marilyn Walker, Heng Ji, and Amanda Stent (Eds.). Association for Computational Linguistics, New Orleans, Louisiana, 809–819
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information. In Findings of the Association for Computational Linguistics: NAACL 2022 . 38–48
Bowen Yang, Cong Han, Yu Li, Lei Zuo, and Zhou Yu. 2022 · 2022
Later among the works it cites.
Preference Elicitation with Soft Attributes in Interactive Recommendation
Erdem Biyik, Fan Yao, Yinlam Chow, Alex Haig, Chih-wei Hsu, Mohammad Ghavamzadeh, and Craig Boutilier. 2023 · 2023
Later among the works it cites.
Leveraging Large Language Models in Conversational Recommender Systems
Luke Friedman, Sameer Ahuja, David Allen, Terry Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, et al · 2023
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Bayesian optimization
Roman Garnett. 2023 · 2023
Later among the works it cites.
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. 2023 · 2023
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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (New Orleans, Louisiana). Association for Computational Linguistics, 1112–1122
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Towards Knowledge-Based Recommender Dialog System. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, Hong Kong, China, 1803–1813
Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, and Jie Tang. 2019 · 2019
Cited alongside, same era.
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19) . Association for Computing Machinery, New York, NY, USA, 1073–1082
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Cited alongside, same era.
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, Hong Kong, China, 3914–3923
Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019 · 2019
Cited alongside, same era.
On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (Eds.). Association for Computational Linguistics, Online, 1906–1919
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 2020
Cited alongside, same era.
Gradient-Based Optimization for Bayesian Preference Elicitation
Ivan Vendrov, Tyler Lu, Qingqing Huang, and Craig Boutilier. 2020 · 2020
Cited alongside, same era.
Conversational Contextual Bandit: Algorithm and Application. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 662–672
Xiaoying Zhang, Hong Xie, Hang Li, and John C.S. Lui. 2020 · 2020
Cited alongside, same era.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users
Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, and Tat-Seng Chua. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Eliciting Human Preferences with Language Models
Belinda Z. Li, Alex Tamkin, Noah Goodman, and Jacob Andreas. 2023 · 2023
Later among the works it cites.
Recipe-MPR: A Test Collection for Evaluating Multi-aspect Preference-based Natural Language Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 2744–2753
Haochen Zhang, Anton Korikov, Parsa Farinneya, Mohammad Mahdi Abdollah Pour, Manasa Bharadwaj, Ali Pesaranghader, Xi Yu Huang, Yi Xin Lok, Zhaoqi Wang, Nathan Jones, and Scott Sanner. 2023 · 2023
Later among the works it cites.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys). In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24), August 25–29, 2024, Barcelona, Spain
Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024 · 2024
Closest in time.
Bayesian Preference Elicitation with Language Models
Kunal Handa, Yarin Gal, Ellie Pavlick, Noah Goodman, Jacob Andreas, Alex Tamkin, and Belinda Z. Li. 2024 · 2024
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Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington, DC, USA) (SIGIR ’24) . ACM, New York, NY, USA
Sara Kemper, Justin Cui, Kai Dicarlantonio, Kathy Lin, Danjie Tang, Anton Korikov, and Scott Sanner. 2024 · 2024
Closest in time.
Multi-Aspect Reviewed-Item Retrieval via LLM Query Decomposition and Aspect Fusion. In Proceedings of the 1st SIGIR’24 Workshop on Information Retrieval’s Role in RAG Systems, July 18, 2024, Washington D.C., USA
Anton Korikov, George Saad, Ethan Baron, Mustafa Khan, Manav Shah, and Scott Sanner. 2024a · 2024
Closest in time.
Large Language Model Driven Recommendation
Anton Korikov, Scott Sanner, Yashar Deldjoo, Francesco Ricci, Zhankui He, Julian McAuley, Arnau Ramisa, Rene Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024b · 2024
Closest in time.
Interactive Path Reasoning on Graph for Conversational Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20) . Association for Computing Machinery, New York, NY, USA, 2073–2083
Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, and Tat-Seng Chua. 2020 · 2083
Closest in time.