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Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system's ability to provide personalized recommendations and improving the overall user experience.
Matrix factorization techniques for recommender systems
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Conversational recommender system
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Improving conversational recommender systems via knowledge graph based semantic fusion
Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-Rong Wen, and Jingsong Yu · 2020
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Advances and challenges in conversational recommender systems: A survey
Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua · 2021
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Unified conversational recommendation policy learning via graph-based reinforcement learning
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Adapting user preference to online feedback in multi-round conversational recommendation
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Learning neural templates for recommender dialogue system
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A survey on large language model based autonomous agents. corr abs/2308.11432 (2023), 2023
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Rethinking the evaluation for conversational recommendation in the era of large language models
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Recagent: A novel simulation paradigm for recommender systems
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The rise and potential of large language model based agents: A survey
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