LLM agents enable users to integrate cross-platform and offline data for personalization that outperforms single-platform baselines in proof-of-concept tests.
Matrix factorization techniques for recom- mender systems.Computer, 42(8):30–37
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RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
citing papers explorer
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LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
LLM agents enable users to integrate cross-platform and offline data for personalization that outperforms single-platform baselines in proof-of-concept tests.
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RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
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Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.