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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BBDRec applies Brownian bridge diffusion to enable direct item-to-history transitions in sequential recommendation, outperforming prior diffusion and sequential baselines on public datasets.
A multi-agent LLM recommender boosts perceived novelty and diversity in movie suggestions, with effects shaped by user conscientiousness, extraversion, GenAI experience, and skepticism.
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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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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.