Jiandong Ding

Principal Algorithm Expert · Huawei Technologies Co. Ltd.

Since 2012, I have worked on applied AI and data systems across IBM, Bosch, Alibaba DAMO Academy, and Huawei. My current research asks how recommendation and AI retrieval systems can remain reliable as users, catalogs, tasks, and interfaces change.

Current focus

Adaptive recommendation · Auditable AI retrieval

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Jiandong Ding portrait
Research

What I study

I study how learning and retrieval systems remain dependable when the evidence they rely on changes.

01

Adaptive Recommendation under Change

I study how recommendation systems should adapt when user evidence becomes stale, catalogs evolve, and production constraints limit what can safely change.

  • Zero-observation user reactivation after long inactivity
  • Generative and sequential recommendation under changing intent
  • Dynamic retrieval, graph learning, and efficient ranking models
Recommender Systems Data Mining
Public code and resources
Code artifact RecSys 2026

DeltaGate

A reproducible implementation of gap-conditioned dimensional gating for zero-observation user reactivation, with frozen sequential backbones, audited experiment runners, and full-ranking evaluation.

Python · PyTorch · MIT License
Repository Digest
02

Auditable AI Retrieval and Interfaces

I study whether AI retrieval systems use the right evidence, expose dependable interfaces, and control misleading or unsafe retrieval behavior before deployment.

  • Search and recommendation interaction and attribution
  • Semantic-ID interface diagnostics for generative recommendation
  • AI search, agent skill retrieval, and capability governance
LLM Agents Recommender Systems
Public code and resources
Diagnostic resource CIKM 2026

SIDInspector

A CPU-ready toolkit for inspecting exported Semantic-ID mappings through utilization, aliasing, neighborhood alignment, popularity allocation, structural cost, and refresh stability.

Python · CPU-ready · MIT License
Repository Digest
Resource package arXiv 2026

SIDScope

An artifact-level resource for auditing Semantic-ID interfaces before downstream generative recommendation, including mapping diagnostics, candidate-exposure probes, and bounded trace accounting.

Python · Reproducibility package · MIT License
Repository Digest
Research record

Selected papers

All publications
Zero-Observation User Reactivation with Gap-Driven Dimensional Gating
RecSys 2026 Adaptive recommendation

Zero-Observation User Reactivation with Gap-Driven Dimensional Gating

DeltaGate recalibrates frozen sequential recommendation representations for long-gap returning users.

Digest
Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations
WWW 2026 Adaptive recommendation

Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations

Hierarchical and preference-aware generative recommendations move beyond flat user histories and expose structured intent over time.

Digest
Invariant feature learning for counterfactual watch-time prediction in video recommendation
AAAI 2026 Adaptive recommendation

Invariant feature learning for counterfactual watch-time prediction in video recommendation

Invariant feature learning separates duration effects from true user preference in short-video recommendation.

Digest
RPE4Rec: Enhancing Dynamic Node Retrieval with Efficient Relative Position Encoding for Recommendation Systems
WSDM 2026 Adaptive recommendation

RPE4Rec: Enhancing Dynamic Node Retrieval with Efficient Relative Position Encoding for Recommendation Systems

Efficient relative position encoding for dynamic node retrieval in recommendation systems.

Digest
BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios
ICSOC 2024 Auditable AI retrieval

BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios

A benchmark for evaluating service-oriented NL2SQL systems in business intelligence scenarios.

Digest
Unified Low-rank Compression Framework for Click-through Rate Prediction
KDD 2024 Adaptive recommendation

Unified Low-rank Compression Framework for Click-through Rate Prediction

A unified compression framework for large CTR models and resource-constrained deployment.

Digest
Contact

Collaboration and exchange

I welcome focused conversations around recommender systems, LLM agents, data mining, shared benchmarks, and applied research problems. I am also recruiting student research interns; please get in touch if your interests align with these areas.

Collaboration record
University collaboration dingjiandong2@huawei.com Research exchange and invited talks jdding@fudan.edu.cn