Zhaoyan Ming

1.4k total citations
44 papers, 888 citations indexed

About

Zhaoyan Ming is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Zhaoyan Ming has authored 44 papers receiving a total of 888 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 14 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Zhaoyan Ming's work include Topic Modeling (12 papers), Expert finding and Q&A systems (7 papers) and Advanced Text Analysis Techniques (7 papers). Zhaoyan Ming is often cited by papers focused on Topic Modeling (12 papers), Expert finding and Q&A systems (7 papers) and Advanced Text Analysis Techniques (7 papers). Zhaoyan Ming collaborates with scholars based in China, Singapore and Macao. Zhaoyan Ming's co-authors include Tat‐Seng Chua, Kai Wang, Hapnes Toba, Mirna Adriani, Xiangnan He, Jinyin Chen, Liqiang Nie, Kai Wang, Kejie Huang and Xian-Ling Mao and has published in prestigious journals such as IEEE Access, BMC Bioinformatics and Pattern Recognition.

In The Last Decade

Zhaoyan Ming

43 papers receiving 851 citations

Peers

Zhaoyan Ming
Comparison fields: 5 of 100
  • Artificial Intelligence 566
  • Information Systems 402
  • Computer Vision and Pattern Recognition 158
  • Computer Science Applications 95
  • Public Health, Environmental and Occupational Health 59
Replace Cheng-Kang Hsieh with:
Cheng-Kang Hsieh United States
Jitao Sang China
Carolina Ruiz United States
Mehrdad Jalali Iran
Thi Ngoc Trang Tran Austria
Cory J. Butz Canada
Xian-Ling Mao China
Amit Pande United States
Fabio Persia Italy
Cheng-Kang Hsieh United States View profile →
Citations per field, relative to Zhaoyan Ming
Zhaoyan Ming · 1×
Citations per year, relative to Zhaoyan Ming
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Countries citing papers authored by Zhaoyan Ming

Since Specialization
Citations

This map shows the geographic impact of Zhaoyan Ming's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zhaoyan Ming with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhaoyan Ming more than expected).

Fields of papers citing papers by Zhaoyan Ming

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhaoyan Ming. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zhaoyan Ming. The network helps show where Zhaoyan Ming may publish in the future.

Co-authorship network of co-authors of Zhaoyan Ming

This figure shows the co-authorship network connecting the top 25 collaborators of Zhaoyan Ming. A scholar is included among the top collaborators of Zhaoyan Ming based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zhaoyan Ming. Zhaoyan Ming is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 4
2 10
3 7
4 1
5 10
6 24
7 13
8 0
9 12
10 37
11 3
12 20
13
Visually-aware Collaborative Food Recommendation.
3
14 6
15
Tackling data sparseness in recommendation using social media based topic hierarchy modeling
3
16 5
17
SSHLDA: A Semi-Supervised Hierarchical Topic Model
36
18
The Use of Dependency Relation Graph to Enhance the Term Weighting in Question Retrieval
16
19 22
20 158

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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