Kun Gai

6.2k total citations · 2 hit papers
68 papers, 2.9k citations indexed

About

Kun Gai is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kun Gai has authored 68 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Information Systems, 34 papers in Artificial Intelligence and 30 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kun Gai's work include Recommender Systems and Techniques (38 papers), Advanced Bandit Algorithms Research (17 papers) and Topic Modeling (9 papers). Kun Gai is often cited by papers focused on Recommender Systems and Techniques (38 papers), Advanced Bandit Algorithms Research (17 papers) and Topic Modeling (9 papers). Kun Gai collaborates with scholars based in China, United States and Hong Kong. Kun Gai's co-authors include Xiaoqiang Zhu, Guorui Zhou, Ying Fan, Xiao Ma, Junqi Jin, Han Li, Yanghui Yan, Zhu Han, Weijie Bian and Na Mou and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neurocomputing and ACM Transactions on Information Systems.

In The Last Decade

Kun Gai

57 papers receiving 2.8k citations

Hit Papers

Deep Interest Network for Click-Through Rate Prediction 2018 2026 2020 2023 2018 2019 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Interest Network for Click-Through Rate Prediction
    2018 1.1k citations Kun Gai et al. profile →
  2. Deep Interest Evolution Network for Click-Through Rate Prediction
    2019 578 citations Kun Gai et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kun Gai China 20 2.0k 1.4k 1.2k 538 357 68 2.9k
Xiaoqiang Zhu China 8 1.8k 0.9× 1.3k 0.9× 823 0.7× 453 0.8× 328 0.9× 15 2.3k
Ying Fan China 9 1.6k 0.8× 1.1k 0.8× 747 0.6× 374 0.7× 303 0.8× 24 2.1k
Xiuqiang He China 27 1.7k 0.8× 1.6k 1.1× 744 0.6× 463 0.9× 238 0.7× 99 2.5k
Ruiming Tang China 26 1.6k 0.8× 1.4k 1.0× 548 0.5× 483 0.9× 205 0.6× 150 2.2k
Chang Zhou China 21 1.3k 0.6× 1.4k 1.0× 769 0.6× 272 0.5× 282 0.8× 53 2.2k
Jun Ma China 28 1.9k 0.9× 1.8k 1.2× 899 0.7× 467 0.9× 261 0.7× 104 3.1k
Zhaochun Ren China 33 2.2k 1.1× 3.1k 2.1× 731 0.6× 455 0.8× 240 0.7× 147 3.9k
Weike Pan China 25 1.7k 0.8× 1.5k 1.0× 524 0.4× 492 0.9× 300 0.8× 108 2.5k
Wenwu Ou China 16 1.6k 0.8× 1.3k 0.9× 546 0.5× 454 0.8× 184 0.5× 30 2.1k
Paolo Cremonesi Italy 23 2.1k 1.0× 1.1k 0.8× 871 0.7× 608 1.1× 483 1.4× 135 2.9k

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-authorship network of co-authors of Kun Gai

This figure shows the co-authorship network connecting the top 25 collaborators of Kun Gai. A scholar is included among the top collaborators of Kun Gai 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 Kun Gai. Kun Gai 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
1.
Jia, Xu, Huchuan Lu, Tianfan Xue, et al.. (2025). CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation. 1–10. 1 indexed citations
2.
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Cao, Jiangxia, et al.. (2025). HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou. 2638–2647. 1 indexed citations
4.
5.
Li, Junyi, et al.. (2024). Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector. 14600–14615. 2 indexed citations
6.
Cao, Jiangxia, et al.. (2024). A Multi-modal Modeling Framework for Cold-start Short-video Recommendation. 391–400. 4 indexed citations
7.
Gao, Chen, Yu Zheng, Jianxin Chang, et al.. (2024). Mixed Attention Network for Cross-domain Sequential Recommendation. 405–413. 13 indexed citations
8.
Yang, Jian, et al.. (2024). Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems. 3798–3809. 2 indexed citations
9.
Zhang, Zijian, Shuchang Liu, Jiaao Yu, et al.. (2024). M 3 oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework. arXiv (Cornell University). 893–902. 12 indexed citations
10.
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Chang, Jianxin, Zhiyi Fu, Xiaoxue Zang, et al.. (2023). TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou. 3785–3794. 15 indexed citations
13.
Li, Biao, et al.. (2023). Disentangled Causal Embedding With Contrastive Learning For Recommender System. 406–410. 10 indexed citations
14.
Liu, Shuchang, Qingpeng Cai, Peng Jiang, et al.. (2023). Exploration and Regularization of the Latent Action Space in Recommendation. arXiv (Cornell University). 833–844. 15 indexed citations
15.
Liu, Ziru, Qingpeng Cai, Xiangyu Zhao, et al.. (2023). Multi-Task Recommendations with Reinforcement Learning. arXiv (Cornell University). 1273–1282. 23 indexed citations
16.
Cai, Qingpeng, et al.. (2023). Two-Stage Constrained Actor-Critic for Short Video Recommendation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 865–875. 22 indexed citations
17.
Chang, Jianxin, et al.. (2023). PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. 3795–3804. 41 indexed citations
18.
Zhang, Xiao, Jun Xu, Xiaoxue Zang, et al.. (2023). When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation. arXiv (Cornell University). 1313–1323. 7 indexed citations
19.
Wang, Chaokun, et al.. (2023). Graph Contrastive Learning with Generative Adversarial Network. arXiv (Cornell University). 2721–2730. 20 indexed citations
20.
Wang, Chaokun, et al.. (2023). Multi-behavior Self-supervised Learning for Recommendation. arXiv (Cornell University). 496–505. 28 indexed citations

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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