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Xin Xin

11 papers hereh-index 13838 citations17 works total

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

author position
  • middle author9
  • last author1

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.IR6
  • cs.LG5
same name
  • Xin Xin — 20 papers, h 8
  • Xin Xin — 10 papers, h 3
  • Xin Xin — 7 papers, h 25
  • Xin Xin — 5 papers, h 8
  • Xin Xin — 3 papers, h 1
  • Xin Xin — 2 papers, 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
20202024
most citedAutoDebias: Learning to Debias for Recommendation

166 citations · 346 across the 9 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

Label Denoising through Cross-Model Agreement

Yu Wang, Xin Xin, Zaiqiao Meng +2

Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optim…

cs.IR2023★ 4 cited

Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community

Qingyao Ai, Ting Bai, Zhao Cao +30

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…

cs.IR2023★ 2 cited

How Graph Convolutions Amplify Popularity Bias for Recommendation?

Jiajia Chen, Jiancan Wu, Jiawei Chen +3

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall acc…

cs.IR2023

Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems

Zhaochun Ren, Na Huang, Yidan Wang +7

Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumul…

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