Unifying Behavior Modeling and Semantic Generation for Generative Recommendation

Generative recommendation (GR) assigns each item content-derived Semantic IDs (SIDs) and employs a sequence-to-sequence model to generate the target item's SIDs based on past user sessions. This paradigm typically outperforms sequential recommendation (SR) methods that rely solely on behavior modeling. However, GR methods suffer from semantic preference bias, where they favor semantically similar items while neglecting items with weak semantic similarity but manifest behavioral relevance--patterns that SR models capture more effectively through behavior modeling. In this work, we propose OMG, a GR method that unifies behavior modeling and semantic generation to produce recommendations that seamlessly integrate both information. First, we introduce Hybrid Behavior-Semantic Encoding, enabling OMG to capture salient user behavioral patterns in addition to semantic correlation when encoding interaction sequences, aiming to recall items with certain behavioral relevance but weak semantic similarity. Then, we propose Behavior-Semantic Latent Reasoning. It iteratively refines the encoder output, progressively transforming it from the behavioral space to the semantic space, and leverages the resulting trajectory to guide SIDs decoding, thereby bridging behavior modeling and semantic generation. Finally, we employ Behavior-Reinforced Semantic Generation, which introduces reinforcement learning with behavior-aware rewards to enhance the model's reasoning capability and better align the decoder's generation process with user behavior. Experiments on benchmark datasets demonstrate that OMG consistently outperforms baseline methods.