Yu Gong (龚禹): h-index, Total Citations, and Citation Map
Yu Gong (龚禹)'s h-index is 16 (17 i10-index, 1,746+ total citations across 26+ publications) according to Google Scholar as of June 2026. Yu Gong (龚禹) is affiliated with TikTok, ByteDance, Alibaba Group, SJTU.
Yu Gong (龚禹) is a researcher affiliated with TikTok, ByteDance, Alibaba Group, SJTU, specializing in Recommender System, LLM, Agent. Their work has been cited 1,746 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Yu Gong (龚禹)'s Citation Metrics
Bibliometric impact based on 26 indexed publications.
- H-Index
- 16
- i10-Index
- 17
- Total Citations
- 1,746
- Citing Countries
- 55
As of June 2026.
Yu Gong (龚禹) has an h-index of 16 and 1,746 total citations across 26 publications, with research cited by institutions in 55 countries.
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Global Impact Map
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IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
2017858
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
49 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered a minimax game framework to unify generative and discriminative information retrieval models, establishing a foundational approach that has been extended to recommendation systems and transfer learning.
The researcher developed deep cascade multi-task learning for slot filling, extending to edge-based recommender systems, evidenced by high independent citation rates.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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