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Qing He

23 papers hereh-index 188.6k citations54 works total

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

author position
  • middle author2
  • last author20

Across the 22 of 23 papers where every author was matched, so the position is known.

fields
  • cs.IR10
  • cs.LG9
  • cs.CV2
  • cs.AI1
  • cs.CL1
same name
  • Qing He — 12 papers, h 38
  • Qing He — 11 papers, h 32
  • Qing He — 9 papers, h 9
  • Qing He — 9 papers, h 20
  • Qing He — 9 papers, h 6
  • Qing He — 5 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
20192023
most citedDeep Subdomain Adaptation Network for Image Classification

1.2k citations · 2k across the 23 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020★ 19 cited

Trust the Model When It Is Confident: Masked Model-based Actor-Critic

Feiyang Pan, Jia He, Dandan Tu +1

It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model err…

cs.LG2020★ 4 cited

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

Dongbo Xi, Bowen Song, Fuzhen Zhuang +5

With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich…

cs.IR2020★ 5 cited

Graph Factorization Machines for Cross-Domain Recommendation

Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu +3

Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result…

cs.IR2020★ 25 cited

A Survey on Knowledge Graph-Based Recommender Systems

Qingyu Guo, Fuzhen Zhuang, Chuan Qin +4

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although…

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