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researcher

Yan Feng

27 papers hereh-index 201.9k citations51 works total

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

author position
  • middle author25

Across the 25 of 27 papers where every author was matched, so the position is known.

fields
  • cs.IR13
  • cs.LG10
  • cs.CV3
  • cs.AI1
same name
  • Yan Feng — 28 papers, h 19
  • Yan Feng — 13 papers, h 6
  • Yan Feng — 8 papers, h 13
  • Yan Feng — 7 papers, h 3
  • Yan Feng — 6 papers, h 9
  • Yan Feng — 6 papers, h 3

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
20192025
most citedHow Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

20 citations · 66 across the 26 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 4 cited

Cross-Layer Distillation with Semantic Calibration

Defang Chen, Jian-Ping Mei, Yuan Zhang +3

Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model. Recently, feature-map based variants expl…

cs.IR2020★ 2 cited

CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation

Jiawei Chen, Chengquan Jiang, Can Wang +5

Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…

cs.IR2020★ 2 cited

SamWalker++: recommendation with informative sampling strategy

Can Wang, Jiawei Chen, Sheng Zhou +3

Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the u…

cs.IR2020

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

Jiawei Chen, Can Wang, Sheng Zhou +4

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendat…

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