{"ID":2857906,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07784","arxiv_id":"2510.07784","title":"PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations","abstract":"Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit significantly from the sequence modeling capabilities and world knowledge inherent in these large models. In this paper, we introduce PLUM, a framework designed to adapt pre-trained LLMs for industry-scale recommendation tasks. PLUM consists of item tokenization using Semantic IDs, continued pre-training (CPT) on domain-specific data, and task-specific fine-tuning for recommendation objectives. For fine-tuning, we focus particularly on generative retrieval, where the model is directly trained to generate Semantic IDs of recommended items based on user context. We conduct comprehensive experiments on large-scale internal video recommendation datasets. Our results demonstrate that PLUM achieves substantial improvements for retrieval compared to a heavily-optimized production model built with large embedding tables. We also present a scaling study for the model's retrieval performance, our learnings about CPT, a few enhancements to Semantic IDs, along with an overview of the training and inference methods that enable launching this framework to billions of users in YouTube.","short_abstract":"Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit significantly from the sequence modeling capabilities and world knowledge inherent in these large models. In this paper, we introduce PLUM, a fra...","url_abs":"https://arxiv.org/abs/2510.07784","url_pdf":"https://arxiv.org/pdf/2510.07784v1","authors":"[\"Ruining He\",\"Lukasz Heldt\",\"Lichan Hong\",\"Raghunandan Keshavan\",\"Shifan Mao\",\"Nikhil Mehta\",\"Zhengyang Su\",\"Alicia Tsai\",\"Yueqi Wang\",\"Shao-Chuan Wang\",\"Xinyang Yi\",\"Lexi Baugher\",\"Baykal Cakici\",\"Ed Chi\",\"Cristos Goodrow\",\"Ningren Han\",\"He Ma\",\"Romer Rosales\",\"Abby Van Soest\",\"Devansh Tandon\",\"Su-Lin Wu\",\"Weilong Yang\",\"Yilin Zheng\"]","published":"2025-10-09T05:01:05Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}