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researcher

Jiandong Zhang

4 papers hereh-index 459 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.IR1
  • cs.LG1
same name
  • Jiandong Zhang — 3 papers, h 3
  • Jiandong Zhang — 2 papers, h 4
  • Jiandong Zhang — 1 paper
  • Jiandong Zhang — 1 paper, h 1
  • Jiandong Zhang — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents

1 citations · 1 across the 2 of their papers we have counts for

collaborators

4 papers

cs.LG2026

Data-dependent Exploration for Online Reinforcement Learning from Human Feedback

Zhen-Yu Zhang, Yuting Tang, Jiandong Zhang +2

Online reinforcement learning from human feedback (RLHF) has emerged as a promising paradigm for aligning large language models (LLMs) by continuously collecting new preference fee…

cs.IR2025

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction

Alin Fan, Hanqing Li, Sihan Lu +2

Modern industrial recommendation systems improve recommendation performance by integrating multimodal representations from pre-trained models into ID-based Click-Through Rate (CTR)…

cs.CL2025★ 1 cited

ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents

Jiangyuan Wang, Kejun Xiao, Qi Sun +4

Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as…

cs.CL2024

In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement

Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao +2

Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality. Most existing methods rely…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.