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. We conduct comprehensive experiments on large-scale internal video recommendation datasets and demonstrate substantial improvements for retrieval compared to a heavily-optimized production model.
