Auto-regressive next-token prediction is strictly equivalent to full-vocabulary maximum likelihood estimation in generative recommendation under bijective item-to-token-sequence mapping.
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cs.IR 3years
2026 3representative citing papers
OCARM uses teacher-student distillation to let retention models learn from inaccessible post-conversion content without feature leakage, yielding improvements in offline experiments and online A/B tests.
RecoChain unifies generative candidate generation via hierarchical semantic IDs and SIM-based ranking in a single Transformer to improve top-K recommendation performance.
citing papers explorer
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On the Equivalence Between Auto-Regressive Next Token Prediction and Full-Item-Vocabulary Maximum Likelihood Estimation in Generative Recommendation--A Short Note
Auto-regressive next-token prediction is strictly equivalent to full-vocabulary maximum likelihood estimation in generative recommendation under bijective item-to-token-sequence mapping.
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Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling
OCARM uses teacher-student distillation to let retention models learn from inaccessible post-conversion content without feature leakage, yielding improvements in offline experiments and online A/B tests.
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Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation
RecoChain unifies generative candidate generation via hierarchical semantic IDs and SIM-based ranking in a single Transformer to improve top-K recommendation performance.