Neighborhood-Aware Unlearning for Generative Recommendation
Generative recommendation predicts items by autoregressively generating semantic identifiers (SIDs), whose tokens are shared across semantically related items. With this paradigm gaining adoption, the question of how to support unlearning in it has become pressing. If training interactions must later be removed, such as injected spam or interactions with items a user no longer wants, this sharing lets an unlearning update unintentionally affect the predictions for the interactions' neighbors. We show that this effect is locally structured and hidden by global metrics: unlearning can leave recommendation utility on the whole test set nearly unchanged while substantially degrading it on nei...
| Authors | Pierre Lubitzsch, Yubao Tang, Maarten de Rijke et al. |
|---|---|
| Primary category | cs.IR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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