ARXIV:2606.03091 · RECOMMENDATION SYSTEMS · SUBMITTED 03 JUN · 20:48 UTC · FRESHNESS FRESH

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

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

Xi Zhou · Famin Wu · Mingming Li · Hongyue Zhang · Jiao Dai · Jizhong Han · +1 at arXiv

A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users. However, the long-tail distribution induces severe signal heterogeneity: dense head sequences trigger the solidification…

METHOD

Full 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 transfer. We propose BAHSD, a black-box adaptive distillation framework that handles signal heterogeneity via a multi-scale consistency probing mechanism to implicitly quantify signal reliability. Based on this, an adaptive hierarchical objective is designed: dynamic-temperature KL divergence mitigates preference solidification for high-confidence signals, while ranking consistency and InfoNCE contrastive learning provide noise-robust enhancement for low-confidence signals. BAHSD consistently outperforms baselines, achieving up to 4.98\% gain over the teacher and 80\%+ improvement on tail users, offering a plug-and-play solution for high-fidelity black-box recommendation extraction.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. BAHSD consistently outperforms baselines, achieving up to 4.98\% gain over the teacher and 80\%+ improvement on tail users, offering a plug-and-play solution for high-fidelity…

WHY NOW

Recommendation Systems moved forward this cycle; last verified June 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainA framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.

Segment

Recommendation Systems

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

{ "contract_version": "paper-r2", "paper_id": "cbbdefbe-cbb6-4c57-9bd0-ccb602c1b25d", "arxiv_id": "2606.03091", "canonical_route": "/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation", "active_tab": "synced from current hash by the drawer client", "selected_artifact": "bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation", "endpoints": { "paper_pack": "/api/v1/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation/paper-pack", "build_passport": "/api/v1/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation/build-passport", "mcp_resource": "sciencetostartup://surfaces/paper-workspace" } }

{ "surface": "paper", "mode": "paper", "query": "BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation", "normalized_query": "2606.03091", "route": "/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation", "paper_ref": "bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation", "topic_slug": null, "benchmark_ref": null, "dataset_ref": null }

{ "@context": "https://schema.org", "@graph": [ { "@type": "WebPage", "@id": "https://sciencetostartup.com/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation#webpage", "url": "https://sciencetostartup.com/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation", "name": "BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation", "description": "A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.", "isPartOf": { "@id": "https://sciencetostartup.com/#website" } }, { "@type": "ScholarlyArticle", "@id": "https://sciencetostartup.com/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation#scholarlyArticle", "headline": "BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation", "description": "A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.", "url": "https://sciencetostartup.com/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation", "sameAs": "https://arxiv.org/abs/2606.03091", "identifier": { "@type": "PropertyValue", "propertyID": "arXiv", "value": "2606.03091" }, "isAccessibleForFree": true, "isPartOf": { "@id": "https://sciencetostartup.com/#website" }, "datePublished": "2026-06-02T03:26:25.000Z", "author": [ { "@type": "Person", "name": "Xi Zhou" }, { "@type": "Person", "name": "Famin Wu" }, { "@type": "Person", "name": "Mingming Li" }, { "@type": "Person", "name": "Hongyue Zhang" }, { "@type": "Person", "name": "Jiao Dai" }, { "@type": "Person", "name": "Jizhong Han" }, { "@type": "Person", "name": "Tao Guo" } ], "additionalProperty": [ { "@type": "PropertyValue", "propertyID": "viabilityScore", "value": 3 }, { "@type": "PropertyValue", "propertyID": "researchDomain", "value": "Recommendation Systems" } ] }, { "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://sciencetostartup.com" }, { "@type": "ListItem", "position": 2, "name": "Recommendation Systems", "item": "https://sciencetostartup.com/topics" }, { "@type": "ListItem", "position": 3, "name": "BAHSD: Bridging the Long-tail Gap via Adaptive Distillation ", "item": "https://sciencetostartup.com/paper/bahsd-bridging-the-long-tail-gap-via-adaptive-distillation-in-black-box-sequential-recommendation" } ] } ] }

Competitive landscape

A framework for adaptive distillation in black-box sequential recommendation systems to handle signal heterogeneity and improve performance on tail users.

Segment

Recommendation Systems

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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

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

Claim map

Constellation map

Competitive landscape

Buzz

PDF

REFERENCES

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

REFERENCES

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline