Ding Zou

432 total citations · 1 hit paper
5 papers, 240 citations indexed

About

Ding Zou is a scholar working on Information Systems, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Ding Zou has authored 5 papers receiving a total of 240 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Information Systems, 5 papers in Artificial Intelligence and 1 paper in Management Science and Operations Research. Recurrent topics in Ding Zou's work include Recommender Systems and Techniques (5 papers), Advanced Graph Neural Networks (4 papers) and Topic Modeling (2 papers). Ding Zou is often cited by papers focused on Recommender Systems and Techniques (5 papers), Advanced Graph Neural Networks (4 papers) and Topic Modeling (2 papers). Ding Zou collaborates with scholars based in China, Singapore and Australia. Ding Zou's co-authors include Xian-Ling Mao, Wei Wei, Ziyang Wang, Feida Zhu, Minghui Qiu, Xin Cao, Sen Zhao, Xiaoli Li, Ruixuan Li and Yifan Liu and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Knowledge and Data Engineering and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

In The Last Decade

Ding Zou

5 papers receiving 237 citations

Hit Papers

Multi-level Cross-view Contrastive Learning for Knowledge... 2022 2026 2023 2024 2022 25 50 75 100

Peers

Ding Zou
Comparison fields: 5 of 30
  • Information Systems 205
  • Artificial Intelligence 202
  • Computer Vision and Pattern Recognition 55
  • Management Science and Operations Research 15
  • Computer Networks and Communications 11
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Suyu Ge China
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Jinze Bai China
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Sarik Ghazarian United States
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Yiheng Shu China
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Suyu Ge China View profile →
Citations per field, relative to Ding Zou
Ding Zou · 1×
Citations per year, relative to Ding Zou
Ding Zou · 1×

Countries citing papers authored by Ding Zou

Since Specialization
Citations

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

Fields of papers citing papers by Ding Zou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ding Zou

This figure shows the co-authorship network connecting the top 25 collaborators of Ding Zou. A scholar is included among the top collaborators of Ding Zou 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 Ding Zou. Ding Zou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
# Work Indexed citations
1 8
2 4
3
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System breakdown →
111
4 55
5 62

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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