R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision

Abstract

Generative recommendation has recently emerged as a promising paradigm for large-scale recommendation by reformulating next-item prediction as discrete sequence generation. However, current SID-based models rely mainly on history-to-next-item supervision, leaving item--item collaborative topology only indirectly captured through sparse user sequences. To address this gap, we propose *R-GRec*, a graph-derived structural supervision framework for SID-based generative recommendation. From user interactions, *R-GRec* builds an item--item graph and derives three complementary auxiliary objectives---Neighbor Prediction, Topological Contrast, and Link Prediction --- to make collaborative topology explicit during generative training. Extensive experiments on the two public Amazon datasets demonstrate that R-GRec achieves state-of-the-art performance over representative traditional, generative, and LLM-based recommenders. Further ablation and analytical studies verify the contribution of each graph-derived objective and show that collaborative topology acts as a complementary supervision signal to Semantic ID generation, improving generative recommendation without adding inference-time graph computation.