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Yuting Liu

18 papers hereh-index 10223 citations26 works total

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

author position
  • first author5
  • middle author11

Across the 16 of 18 papers where every author was matched, so the position is known.

fields
  • cs.IR11
  • cs.CL5
  • cs.LG2
same name
  • Yuting Liu — 25 papers, h 15
  • Yuting Liu — 10 papers, h 7
  • Yuting Liu — 4 papers, h 7
  • Yuting Liu — 3 papers, h 8
  • Yuting Liu — 3 papers, h 10
  • Yuting Liu — 2 papers, h 2

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
20232026
most citedStealthy Attack on Large Language Model based Recommendation

2 citations · 6 across the 17 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.IR2025

RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation

Run Ling, Wenji Wang, Yuting Liu +12

Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effect…

cs.IR2025

Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation

Yizhou Dang, Yuting Liu, Enneng Yang +4

Data augmentation has become a promising method of mitigating data sparsity in sequential recommendation. Existing methods generate new yet effective data during model training to…

cs.IR2025

Hard Negative Sampling via Large Language Models for Recommendation

Chu Zhao, Enneng Yang, Yuting Liu +2

Hard negative sampling improves recommendation performance by accelerating convergence and sharpening the decision boundary. However, most existing methods rely on heuristic strate…

cs.CL2025

Personalized Text Generation with Contrastive Activation Steering

Jinghao Zhang, Yuting Liu, Wenjie Wang +4

Personalized text generation aims to infer users' writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics…

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