Kun Gai

6.3k citations
72 papers · 3.1k · 2 hit papers · h-index 20

Impact in

Papers in

Kun Gai

60 papers receiving 3.0k citations

Kun Gai's Hit Papers

Deep Interest Evolution Network for Click-Through Rate Prediction 2019 · 602 citations
6020+2+5Years since publication2505007501000

Peers

Kun Gai
Comparison fields: 5 of 100
  • Information Systems 2.1k
  • Computer Vision and Pattern Recognition 1.3k
  • Artificial Intelligence 1.5k
  • Management Science and Operations Research 553
  • Marketing 224
Replace Xiaoqiang Zhu with:
Xiaoqiang Zhu China
Ying Fan China
Xiuqiang He China
Jeremiah Harmsen United States
Ruiming Tang China
Hemal Shah United States
Wenwu Ou China
Chang Zhou China
Zhaochun Ren China
Jun Ma China
Kun Gai relative to Xiaoqiang Zhu China Xiaoqiang Zhu's profile →
Citations per field
00.5×
Xiaoqiang Zhu · 1×
Citations per year

Countries citing papers authored by Kun Gai

Since Specialization
Citations

This map shows the geographic impact of Kun Gai'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 Kun Gai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kun Gai more than expected).

Fields of papers citing papers by Kun Gai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kun Gai. 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 Kun Gai. The network helps show where Kun Gai may publish in the future.

Co-authors

The 25 scholars most cited alongside Kun Gai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kun Gai Line = papers co-authored together Kun Gai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Interest Network for Click-Through Rate Prediction
Hit paper breakdown →
20181177
2
Deep Interest Evolution Network for Click-Through Rate Prediction
Hit paper breakdown →
2019602
3 2018221
4 2020139
5 201186
6 202080
7 201880
8 201877
9 202352
10 202244
11 202235
12 201934
13 202331
14 202328
15
Learning Kernels with Radiuses of Minimum Enclosing Balls
201026
16 200826
17 201825
18 202322
19 202321
20 202320

About Kun Gai

Kun Gai is a scholar working on Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research and Computer Networks and Communications, having authored 72 papers that have together received 3.1k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (39 papers), Advanced Bandit Algorithms Research (17 papers), Topic Modeling (10 papers), Advanced Graph Neural Networks (9 papers), Image Retrieval and Classification Techniques (8 papers), Image and Video Quality Assessment (7 papers), Caching and Content Delivery (5 papers) and Natural Language Processing Techniques (4 papers). The work is most often cited by research in Information Systems (2.1k citations), Computer Vision and Pattern Recognition (1.3k citations), Artificial Intelligence (1.5k citations), Management Science and Operations Research (553 citations) and Marketing (224 citations). Kun Gai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiaoqiang Zhu, Guorui Zhou, Ying Fan, Xiao Ma, Junqi Jin, Zhu Han, Han Li, Yanghui Yan, Weijie Bian and Na Mou. Their work appears in journals such as ACM Transactions on Information Systems, Neurocomputing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009 IEEE Conference on Computer Vision and Pattern Recognition and DR-NTU (Nanyang Technological University).

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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