Na Mou

1.2k citations
7 papers · 657 · 1 hit paper · h-index 4

Impact in

Papers in

Na Mou

7 papers receiving 626 citations

Na Mou's Hit Papers

Deep Interest Evolution Network for Click-Through Rate Prediction 2019 · 602 citations
6020+2+4Years since publication200400600

Peers

Na Mou
Comparison fields: 5 of 47
  • Information Systems 547
  • Computer Vision and Pattern Recognition 236
  • Artificial Intelligence 341
  • Management Science and Operations Research 108
  • Transportation 40
Replace Jiahui Liu with:
Jiahui Liu China
Keping Yang China
Refuoe Mokhosi China
Bo Long United States
Mohammad Yahya H. Al-Shamri Saudi Arabia
Ziwei Fan China
Jiarui Qin China
Zihan Lin China
Changxin Tian China
Shengxian Wan China
Na Mou relative to Jiahui Liu China Jiahui Liu's profile →
Citations per field
00.5×5.1×
Jiahui Liu · 1×
Citations per year

Countries citing papers authored by Na Mou

Since Specialization
Citations

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

Fields of papers citing papers by Na Mou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Na Mou, 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 Na Mou Line = papers co-authored together Na Mou links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown
#Work
1
Deep Interest Evolution Network for Click-Through Rate Prediction
Hit paper breakdown →
2019602
2 202241
3 20235
4 20253
5 20243
6 20252
7 20141

About Na Mou

Na Mou is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence, Urban Studies and Sociology and Political Science, having authored 7 papers that have together received 657 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (6 papers), Image Retrieval and Classification Techniques (3 papers), Advanced Computing and Algorithms (1 paper), Machine Learning and Algorithms (1 paper), Advanced Manufacturing and Logistics Optimization (1 paper), Advanced Graph Neural Networks (1 paper), Digital Marketing and Social Media (1 paper) and Advanced Bandit Algorithms Research (1 paper). The work is most often cited by research in Information Systems (547 citations), Computer Vision and Pattern Recognition (236 citations), Artificial Intelligence (341 citations), Management Science and Operations Research (108 citations) and Transportation (40 citations). Na Mou has collaborated with scholars based in China and United States. Frequent co-authors include Weijie Bian, Guorui Zhou, Kun Gai, Xiaoqiang Zhu, Chang Zhou, Ying Fan, Xiaoqiang Zhu, Zhangming Chan, Yujing Zhang and Xiang-Rong Sheng. Their work appears in journals such as Applied Mechanics and Materials and Proceedings of the AAAI Conference on Artificial Intelligence.

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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