AI RESEARCH
Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation
arXiv CS.LG
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ArXi:2510.05125v2 Announce Type: replace-cross While collaborative filtering delivers predictive accuracy and efficiency, and Large Language Models (LLMs) enable expressive and generalizable reasoning, modern recommendation systems must bring these strengths together. Growing user expectations, such as natural-language queries and transparent explanations, further highlight the need for a unified approach. However, doing so is nontrivial.