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Solving Continual Combinatorial Selection via Deep Reinforcement Learning.

, , , , , , , and . IJCAI, page 3467-3474. ijcai.org, (2019)

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Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation., , , and . NAACL-HLT, page 1490-1504. Association for Computational Linguistics, (2024)Structural Patterns and Generative Models of Real-world Hypergraphs., , , and . KDD, page 176-186. ACM, (2020)Imagery as Inquiry: Exploring A Multimodal Dataset for Conversational Recommendation., , and . CoRR, (2024)Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation., , and . CIKM, page 973-982. ACM, (2024)Forecasting Live Chat Intent from Browsing History., , , , and . CIKM, page 4223-4227. ACM, (2024)Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking., , , , , , , , , and . CoRR, (February 2025)OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation., , , , , , , and . CoRR, (2024)How Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction., , , and . WWW, page 2627-2633. ACM / IW3C2, (2020)Solving Continual Combinatorial Selection via Deep Reinforcement Learning., , , , , , , and . IJCAI, page 3467-3474. ijcai.org, (2019)