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LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and language generation share a similarity: both aim to produce an ordered sequence that optimizes the user's experience. However, how to precisely absorb the essence of the LLM paradigm into mature industrial recommender systems remains an open problem. There are two challenges. First, it is unclear how to incorporate sequence-level generation and optimization from the LLM paradigm into recommendation. Second, real-world recomme

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