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researcher

Ming Li

9 papers hereh-index 5143 citations12 works total

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

author position
  • sole author1
  • first author4
  • middle author3
  • last author1

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.IR2
  • cs.LG2
  • cs.AI1
  • cs.GR1
  • cs.SE1
same name
  • Ming Li — 85 papers, h 82
  • Ming Li — 66 papers, h 34
  • Ming Li — 47 papers, h 22
  • Ming Li — 43 papers, h 15
  • Ming Li — 41 papers, h 18
  • Ming Li — 26 papers, h 10

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 citedMetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios

11 citations · 26 across the 9 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021★ 11 cited

MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios

Xiaofeng Pan, Ming Li, Jing Zhang +5

Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctua…

cs.CV2021

Automatic annotation of visual deep neural networks

Ming Li, ChenHao Guo

Computer vision is widely used in the fields of driverless, face recognition and 3D reconstruction as a technology to help or replace human eye perception images or multidimensiona…

cs.IR2021★ 4 cited

A Next Basket Recommendation Reality Check

Ming Li, Sami Jullien, Mozhdeh Ariannezhad +1

The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. Recently, a number of meth…

cs.AI2021★ 1 cited

A Data-Driven Method for Recognizing Automated Negotiation Strategies

Ming Li, Pradeep K. Murukannaiah, Catholijn M. Jonker

Understanding an opponent agent helps in negotiating with it. Existing works on understanding opponents focus on preference modeling (or estimating the opponent's utility function)…

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