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This paper systematically investigates Semantic IDs (SIDs) in generative recommendation, finding that while SIDs preserve coarse item organization, they lose fine local structure from the encoder. The authors propose Item-Supported Decoding (ISD), a lightweight inference-time method that improves NDCG@10 by up to 31.2% without additional parameters or retraining.
This paper proposes PauseRec, a lightweight implicit reasoning paradigm for LLM-based generative recommendation that outperforms explicit chain-of-thought methods while significantly reducing training and inference costs.