BLOGER is a bi-level optimization framework that jointly optimizes the tokenizer and recommender for generative recommendation, outperforming prior methods on real-world datasets.
Exploring the impact of personality traits on conversational recommender systems: A simulation with large language models.arXiv preprint arXiv:2504.12313, 2025
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BBDRec applies Brownian bridge diffusion to enable direct item-to-history transitions in sequential recommendation, outperforming prior diffusion and sequential baselines on public datasets.
PUMA applies the Free Energy Principle to maintain beliefs over latent user states and select actions by minimizing expected free energy in multi-turn personalized dialogues.
A multi-agent LLM recommender boosts perceived novelty and diversity in movie suggestions, with effects shaped by user conscientiousness, extraversion, GenAI experience, and skepticism.
citing papers explorer
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Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation
BLOGER is a bi-level optimization framework that jointly optimizes the tokenizer and recommender for generative recommendation, outperforming prior methods on real-world datasets.
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Brownian Bridge Diffusion for Sequential Recommendation
BBDRec applies Brownian bridge diffusion to enable direct item-to-history transitions in sequential recommendation, outperforming prior diffusion and sequential baselines on public datasets.
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Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
PUMA applies the Free Energy Principle to maintain beliefs over latent user states and select actions by minimizing expected free energy in multi-turn personalized dialogues.
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How Personal Characteristics Shape User Exploration of Diverse Movie Recommendations with a LLM-Based Multi-Agent System
A multi-agent LLM recommender boosts perceived novelty and diversity in movie suggestions, with effects shaped by user conscientiousness, extraversion, GenAI experience, and skepticism.