Reasoning over Semantic IDs Enhances Generative Recommendation

The paper presents SIDReasoner, a two-stage framework designed to enhance generative recommendation by improving reasoning over Semantic IDs (SIDs) using pretrained LLMs. It employs multi-task training on a synthesized SID-centered corpus to strengthen SID-language alignment and utilizes outcome-driven reinforced optimization to guide reasoning trajectories without needing explicit annotations. This approach not only boosts recommendation accuracy but also enhances interpretability and cross-domain generalization, offering valuable insights for practitioners working with LLMs in recommendation systems.