BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation
arXiv:2606.03091v1 Announce Type: cross Abstract: Sequential recommendation systems are widely adopted but often deployed as black-box APIs, which has driven recent interest in model extraction to replicate their capabilities locally. However, the long-tail distribution induces severe signal heterogeneity: dense head sequences trigger the solidification of teacher preference, biasing extraction toward local patterns, while sparse tail sequences yield flat, noisy predictions. Existing one-size-fits-all extraction overlooks this disparity, resulting in noise overfitting and suboptimal knowledge
The increasing prevalence of black-box AI recommendation APIs necessitates methods for understanding and replicating their behavior to improve transparency and local model performance.
This research addresses a critical challenge in AI model extraction, particularly for recommendation systems, by improving the handling of diverse data distributions like long-tails, thus enhancing the fidelity and efficiency of extracted models.
The proposed BAHSD method offers a more nuanced approach to model extraction from black-box recommendation systems, leading to better adaptive distillation and more accurate local replicas, especially for sparser data.
- · AI developers
- · Companies using recommendation systems
- · Researchers in model extraction
- · Providers of simple black-box APIs
- · Legacy extraction methods
Improved local replication of black-box recommendation systems will allow for more tailored and efficient deployments.
Enhanced model extraction could lead to greater competition in recommendation system services as their underlying mechanisms become more accessible.
The ability to better understand and replicate black-box models might foster new regulatory discussions around intellectual property and transparency in AI.
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Read at arXiv cs.AI