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researcher

Qing Li

15 papers hereh-index 212.1k citations27 works total

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

author position
  • middle author6
  • last author8

Across the 14 of 15 papers where every author was matched, so the position is known.

fields
  • cs.IR9
  • cs.LG4
  • cs.CL2
same name
  • Qing Li — 43 papers, h 56
  • Qing Li — 30 papers, h 15
  • Qing Li — 22 papers, h 16
  • Qing Li — 18 papers, h 7
  • Qing Li — 16 papers, h 19
  • Qing Li — 15 papers, h 23

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
20192024
most citedRecommender Systems in the Era of Large Language Models (LLMs)

422 citations · 679 across the 15 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 9 cited

Generative Diffusion Models on Graphs: Methods and Applications

Chengyi Liu, Wenqi Fan, Yunqing Liu +5

Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video…

cs.LG2022★ 6 cited

Fairness Reprogramming

Guanhua Zhang, Yihua Zhang, Yang Zhang +4

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the…

cs.LG2022★ 46 cited

Knowledge-enhanced Black-box Attacks for Recommendations

Jingfan Chen, Wenqi Fan, Guanghui Zhu +4

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles…

cs.LG2021★ 6 cited

Jointly Attacking Graph Neural Network and its Explanations

Wenqi Fan, Wei Jin, Xiaorui Liu +7

Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…

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