How Well Does Generative Recommendation Generalize?
A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. However, there is few systematic way to verify this hypothesis beyond a superficial comparison of overall performance. To address this gap, we categorize each data instance based on the specific capability required for a correct prediction: either memorization (reusing item transition patterns observed during training) or generalization (composing known patterns to predict unseen item transitions). Extensive experiments show that GR models perform better on instances that require generalization, whereas item ID-based models perform better when memorization is more important. To explain this divergence, we shift the analysis from the item level to the token level and show that what appears to be item-level generalization often reduces to token-level memorization for GR models. Finally, we show that the two paradigms are complementary. We propose a simple memorization-aware indicator that adaptively combines them on a per-instance basis, leading to improved overall recommendation performance.
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Yijie Ding
claimed_verified
2026-03-23T09:16:19.244Z
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James Ding
jamesding0302
user
jamesding0302
69c0aa243fd3fef6237a6850
Zitian Guo
claimed_verified
2026-03-23T09:16:16.803Z
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Zitian Guo
ztguo
user
ztguo
69c0aa243fd3fef6237a6851
Jiacheng Li
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Letian Peng
claimed_verified
2026-03-23T09:16:13.388Z
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Letian Peng
KomeijiForce
user
KomeijiForce
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Shuai Shao
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Wei Shao
69c0aa243fd3fef6237a6855
Xiaoqiang Luo
69c0aa243fd3fef6237a6856
Luke Simon
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Jingbo Shang
69c0aa243fd3fef6237a6858
Julian McAuley
69c0aa243fd3fef6237a6859
Yupeng Hou
How Well Does Generative Recommendation Generalize?
A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. However, there is few systematic way to verify this hypothesis beyond a superficial comparison of overall performance. To address this gap, we categorize each data instance based on the specific capability required for a correct prediction: either memorization (reusing item transition patterns observed during training) or generalization (composing known patterns to predict unseen item transitions). Extensive experiments show that GR models perform better on instances that require generalization, whereas item ID-based models perform better when memorization is more important. To explain this divergence, we shift the analysis from the item level to the token level and show that what appears to be item-level generalization often reduces to token-level memorization for GR models. Finally, we show that the two paradigms are complementary. We propose a simple memorization-aware indicator that adaptively combines them on a per-instance basis, leading to improved overall recommendation performance.
Generative recommendation models excel at generalization tasks while item ID-based models perform better at memorization, with a complementary approach showing improved recommendation performance through adaptive combination.
generative recommendation item ID-based models generalization memorization token-level analysis item-level analysis recommendation performance