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Junda Wu

55 papers hereh-index 15708 citations65 works total

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

author position
  • first author9
  • middle author36
  • last author5

Across the 50 of 55 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.AI10
  • cs.CL10
  • cs.CV6
  • cs.SD6
  • cs.IR4
same name
  • Junda Wu — 10 papers, h 13
  • Junda Wu — 2 papers, h 1
  • Junda Wu — 1 paper
  • Junda Wu — 1 paper
  • Junda Wu — 1 paper
  • Junda Wu — 1 paper

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 citedVisual Prompting in Multimodal Large Language Models: A Survey

4 citations · 9 across the 49 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2026

SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents

Qianfeng Wen, Yifan Simon Liu, Xin Liu +4

Generative Engine Optimization (GEO) lets content owners rewrite web content to increase their visibility in generative systems. In recommendation agents, this creates a risk that…

cs.IR2026

Evaluation on Entity Matching in Recommender Systems

Zihan Huang, Rohan Surana, Zhouhang Xie +3

Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge-based recommender systems. However, the lack…

cs.IR2025

Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction

Hongtao Huang, Chengkai Huang, Junda Wu +3

Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…

cs.IR2025

A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms

Chengkai Huang, Hongtao Huang, Tong Yu +6

Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…

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