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Min Zhang

9 papers hereh-index 5169 citations13 works total

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

author position
  • middle author2
  • last author5

Across the 7 of 9 papers where every author was matched, so the position is known.

fields
  • cs.IR5
  • cs.CL4
same name
  • Min Zhang — 49 papers, h 11
  • Min Zhang — 32 papers, h 11
  • Min Zhang — 27 papers, h 7
  • Min Zhang — 25 papers, h 6
  • Min Zhang — 24 papers, h 26
  • Min Zhang — 23 papers, h 9

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 citedMACRec: a Multi-Agent Collaboration Framework for Recommendation

48 citations · 50 across the 9 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.IR2026

SelfDR: Self-Distillation from Reasoning for LLM-Based Recommendation

Chumeng Jiang, Jiayin Wang, Xinjie Lin +3

Large Language Models (LLMs) have recently emerged as powerful backbones for recommendation. To better elicit their capabilities, reasoning has been widely incorporated to help LLM…

cs.CL2026

Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection

Yutong Wang, Xuebo Liu, Derek F. Wong +5

Document-level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that impede global cohesion, while…

cs.CL2026

User-Aware Active Knowledge Acquisition for Emotional Support Dialogue

Mufan Xu, Kehai Chen, Jiahao Hu +4

Emotional support plays an important role in dialogue systems, and its success depends on adapting to a user's evolving and implicit needs across multi-turn interactions while leve…

cs.CL2026

Personalized Turn-Level User Conversation Satisfaction Benchmark

Zhefan Wang, Zhiqiang Guo, Weizhi Ma +3

User satisfaction with AI assistants is highly personalized: the same response may satisfy one user but disappoint another depending on what each user expects and what they have as…

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