Principal Algorithm Expert
Jiandong Ding
I work on recommender systems, LLM agents, and data mining, with a focus on turning research ideas into reliable AI systems at production scale.
Research mainline
My work has moved from structured biological data, through robust learning and analytics, to recommendation and agent systems used at industrial scale.
Zero-observation user reactivation, generative recommendation, dynamic retrieval, semantic-ID diagnostics, and agent skill retrieval.
BI/NL2SQL evaluation, LLM serving, CTR model efficiency, and large-scale recommendation infrastructure.
Continual graph learning, neural topic modeling, weak supervision, robust learning, and live-streaming field experiments.
miRNA target prediction, genome-scale sequence signals, and early structured data mining.
Selected papers
Representative papers that mark the main research line across recommendation, LLM agents, and data mining.
Zero-Observation User Reactivation with Gap-Driven Dimensional Gating
DeltaGate recalibrates frozen sequential recommendation representations for long-gap returning users.
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.
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.
RPE4Rec: Enhancing Dynamic Node Retrieval with Efficient Relative Position Encoding for Recommendation Systems
Efficient relative position encoding for dynamic node retrieval in recommendation systems.
BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios
A benchmark for evaluating service-oriented NL2SQL systems in business intelligence scenarios.
Unified Low-rank Compression Framework for Click-through Rate Prediction
A unified compression framework for large CTR models and resource-constrained deployment.
Active projects
Current research directions are intentionally described at a high level; mature public outputs remain in Full publications and Patents.
Dormant-user recommendation
Studying recommendation for returning users after long inactivity gaps, where pre-gap history exists but its current reliability must be recalibrated.
Search and recommendation interaction
Studying how search behavior and recommendation exposure interact, with emphasis on attribution boundaries rather than headline metric movement.
Semantic-ID auditing
Auditing semantic-ID mappings used by generative recommendation systems before expensive downstream training and deployment decisions.
Agent skill retrieval and governance
Studying retrieval and governance problems when agent skills become reusable software assets in larger enterprise skill libraries.
Collaboration and exchange
Open to university collaboration, research exchange, invited talks, and focused discussions around recommendation, agent systems, and data intelligence.
Academic collaboration
University collaboration, joint research, resource building, benchmark design, and student or lab exchange.
Industrial research
Recommendation architecture, agent evaluation, retrieval systems, and data-intelligence problems at production scale.
Invited talks
Research talks and professional events on recommender systems, LLM agents, and data mining.