BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, long

Summary

BAHSD, a black-box adaptive distillation framework for sequential recommendation systems, addresses signal heterogeneity caused by long-tail user interactions, where dense "head" sequences lead to "preference solidification" (overfitting to local patterns) and sparse "tail" sequences result in "information vacuum" (noisy, flat predictions). BAHSD uses a multi-scale consistency probing mechanism to implicitly quantify signal reliability. It then applies an adaptive hierarchical objective: dynamic-temperature KL divergence for high-confidence signals to mitigate solidification, and ranking consistency with InfoNCE contrastive learning for low-confidence signals to enhance noise-robustness. Experiments on Amazon Beauty and MovieLens-1M datasets, using SASRec and BERT4Rec backbones, show BAHSD consistently outperforms baselines, achieving up to a 4.98% gain over the teacher model and over 80% improvement for tail users. It offers a plug-and-play solution for high-fidelity black-box recommendation extraction.

Key takeaway

For Machine Learning Engineers deploying black-box sequential recommendation APIs, you should consider adaptive distillation frameworks like BAHSD. This approach effectively addresses signal heterogeneity from long-tail user data, preventing overfitting on dense head sequences and enhancing performance on sparse tail sequences. Implementing multi-scale probing and a hierarchical objective can significantly improve model fidelity and tail-user accuracy, reducing query costs and enabling local customization.

Key insights

Black-box sequential recommendation distillation benefits from adaptively handling signal heterogeneity via multi-scale probing and a hierarchical objective.

Principles

• Signal heterogeneity impacts black-box distillation.
• Head users show preference solidification.
• Tail users suffer from information vacuum.

Method

BAHSD uses multi-scale consistency probing to quantify signal reliability. It then applies an adaptive hierarchical objective: dynamic-temperature KL divergence for high-confidence signals and ranking consistency with InfoNCE for low-confidence signals.

In practice

• Use multi-scale sequence probing for reliability.
• Apply dynamic-temperature KL for dense signals.
• Employ InfoNCE for sparse, noisy signals.

Topics

• Black-box Knowledge Distillation
• Sequential Recommendation
• Long-tail Problem
• Adaptive Distillation
• InfoNCE Contrastive Learning
• Transformer Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.