Wenlin Zhang

Orcid: 0000-0003-1809-8264

Affiliations:

City University of Hong Kong, Department of Data Science, Hong Kong

According to our database¹, Wenlin Zhang authored at least 25 papers between 2022 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

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Bibliography

2026

A Survey of Personalization: From RAG to Agent.

[BibT_eX]

[DOI]

ACM Trans. Inf. Syst., May, 2026

T-GINEE: A Tensor-Based Multilayer Graph Representation Learning.

[BibT_eX]

[DOI]

CoRR, May, 2026

RAGR: Review-Augmented Generative Recommendation.

[BibT_eX]

[DOI]

CoRR, May, 2026

Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery.

[BibT_eX]

[DOI]

CoRR, May, 2026

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory.

[BibT_eX]

[DOI]

CoRR, May, 2026

Job Skill Extraction via LLM-Centric Multi-Module Framework.

[BibT_eX]

[DOI]

CoRR, April, 2026

GeoRouter: Dynamic Paradigm Routing for Worldwide Image Geolocalization.

[BibT_eX]

[DOI]

CoRR, March, 2026

Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval.

[BibT_eX]

[DOI]

CoRR, March, 2026

Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing.

[BibT_eX]

[DOI]

CoRR, January, 2026

To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention.

[BibT_eX]

[DOI]

Proceedings of the ACM Web Conference 2026, 2026

NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations.

[BibT_eX]

[DOI]

Proceedings of the ACM Web Conference 2026, 2026

Exploring Recommender System Evaluation: A Multi-Modal LLM Agent Framework for A/B Testing.

[BibT_eX]

[DOI]

Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, 2026

MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search.

[BibT_eX]

[DOI]

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory.

[BibT_eX]

[DOI]

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

MTA: A Merge-then-Adapt Framework for Personalized Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval.

[BibT_eX]

[DOI]

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

MTA: A Merge-then-Adapt Framework for Personalized Large Language Model.

[BibT_eX]

[DOI]

CoRR, November, 2025

Deep Research: A Survey of Autonomous Research Agents.

[BibT_eX]

[DOI]

CoRR, August, 2025

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks.

[BibT_eX]

[DOI]

CoRR, June, 2025

Towards Multi-Granularity Memory Association and Selection for Long-Term Conversational Agents.

[BibT_eX]

[DOI]

CoRR, May, 2025

Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, May, 2025

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations.

[BibT_eX]

[DOI]

Proceedings of the 31st International Conference on Computational Linguistics, 2025

2024

Tired of Plugins? Large Language Models Can Be End-To-End Recommenders.

[BibT_eX]

[DOI]

CoRR, 2024

2022

Graph Convolution RPCA With Adaptive Graph.

[BibT_eX]

[DOI]

IEEE Trans. Image Process., 2022

Robust kernel principal component analysis with optimal mean.

[BibT_eX]

[DOI]

Neural Networks, 2022

Wenlin Zhang

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