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Clustering of Conversational Bandits for User Preference Learning and Elicitation., , , , and . CIKM, page 2129-2139. ACM, (2021)CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation., , , , , , and . CoRR, (2024)InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment., , , , , and . CoRR, (2024)Graph-DPP: Sampling Diverse Neighboring Nodes via Determinantal Point Process., , , and . WI/IAT, page 540-545. IEEE, (2020)List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs., , , , , , , , , and 1 other author(s). CoRR, (2024)Clustering of conversational bandits with posterior sampling for user preference learning and elicitation., , , , and . User Model. User Adapt. Interact., 33 (5): 1065-1112 (November 2023)An Effective Data Creation Pipeline to Generate High-quality Financial Instruction Data for Large Language Model., , , and . CoRR, (2023)User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback., , , , , and . KDD, page 2694-2704. ACM, (2023)Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey., , , , , , , , , and 3 other author(s). CoRR, (2024)Spatial-Temporal Aligned Multi-Agent Learning for Visual Dialog Systems., , , , and . ACM Multimedia, page 482-490. ACM, (2022)