Transformer-based sequential recommenders exhibit power-law and saturating NDCG scaling with model size and training interactions, enabling compute-aware model selection and effective pre-train/fine-tune transfer.
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Scaling Sequential Recommendation Models with Transformers
Transformer-based sequential recommenders exhibit power-law and saturating NDCG scaling with model size and training interactions, enabling compute-aware model selection and effective pre-train/fine-tune transfer.