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Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues.
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Towards Topic-Guided Conversational Recommender System. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain, December 8-11, 2020
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A survey on conversational recommender systems
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Reasoning with Language Model Prompting: A Survey
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Deep Learning for Click-Through Rate Estimation (IJCAI ’21)
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CRSLab: An open-source toolkit for building conversational recommender system
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Bundle MCR: Towards conversational bundle recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems . 288–298
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Towards Universal Sequence Representation Learning for Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 585–593
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User-centric conversational recommendation with multi-aspect user modeling. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 223–233
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Neural Re-ranking in Multi-stage Recommender Systems: A Review
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Multiple choice questions based multi-interest policy learning for conversational recommendation. In Proceedings of the ACM Web Conference 2022 . 2153–2162
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Hangyu Wang, Jianghao Lin, Xiangyang Li, Bo Chen, Chenxu Zhu, Ruiming Tang, Weinan Zhang, and Yong Yu. 2023a · 2023
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Xi Wang, Hossein A Rahmani, Jiqun Liu, and Emine Yilmaz. 2023b · 2023
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Rethinking the evaluation for conversational recommendation in the era of large language models
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A Survey on Large Language Models for Recommendation
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Towards open-world recommendation with knowledge augmentation from large language models
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Variational reasoning over incomplete knowledge graphs for conversational recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining . 231–239
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A survey of large language models
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Large language models for information retrieval: A survey
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Can We Use Large Language Models to Fill Relevance Judgment Holes?
Zahra Abbasiantaeb, Chuan Meng, Leif Azzopardi, and Mohammad Aliannejadi. 2024 · 2024
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ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . 3319–3330
Jianghao Lin, Bo Chen, Hangyu Wang, Yunjia Xi, Yanru Qu, Xinyi Dai, Kangning Zhang, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024a · 2024
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ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . 3497–3508
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Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
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Large Language Models as Conversational Movie Recommenders: A User Study
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LLMs Can Patch Up Missing Relevance Judgments in Evaluation
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Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems
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Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation
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Large Language Models Are Semi-Parametric Reinforcement Learning Agents
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Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 19632–19642
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Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 19724–19731
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Lixi Zhu, Xiaowen Huang, and Jitao Sang. 2024 · 2024
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