Quan Lu
- Artificial Intelligence top 10%
- Information Systems top 10%
- Management Science and Operations Research
- Marketing
- Computer Vision and Pattern Recognition
- Co-authors
- Longtao HuangHe ZhaoRong ZhangHui XueHongxia YangJunwei PanShengjun PanLiang Wang
- Topics
- Recommender Systems and Techniques (8 papers)Advanced Graph Neural Networks (5 papers)Consumer Market Behavior and Pricing (4 papers)
- Journals
- IEEE AccessEngineering Applications of Artificial IntelligenceACM Transactions on Information Systems
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Quan Lu
20 papers receiving 221 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 165
- Information Systems 55
- Management Science and Operations Research 35
- Marketing 25
- Computer Vision and Pattern Recognition 23
Countries citing papers authored by Quan Lu
This map shows the geographic impact of Quan Lu'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 Quan Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quan Lu more than expected).
Fields of papers citing papers by Quan Lu
This network shows the impact of papers produced by Quan Lu. 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 Quan Lu. The network helps show where Quan Lu may publish in the future.
Co-authorship network of co-authors of Quan Lu
This figure shows the co-authorship network connecting the top 25 collaborators of Quan Lu. A scholar is included among the top collaborators of Quan Lu 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 Quan Lu. Quan Lu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 7 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 10 | |
| 11 | 4 | |
| 12 | 2 | |
| 13 | 121 | |
| 14 | 2 | |
| 15 | 3 | |
| 16 | Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features | 3 |
| 17 | 11 | |
| 18 | 1 | |
| 19 | Scalable Multidimensional Hierarchical Bayesian modeling on Spark | 3 |
| 20 | Fast Algorithm of the Triangular Factorization for the Symmetric Loewner Type Matrix | 1 |
About Quan Lu
Quan Lu is a scholar working on Computational Mathematics, Marketing and Information Systems, having authored 24 papers that have together received 229 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (8 papers), Advanced Graph Neural Networks (5 papers) and Consumer Market Behavior and Pricing (4 papers). The work is most often cited by research in Artificial Intelligence (165 citations), Management Science and Operations Research (35 citations) and Marketing (25 citations). Quan Lu has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Longtao Huang, He Zhao, Rong Zhang, Hui Xue, Hongxia Yang, Junwei Pan, Shengjun Pan, Liang Wang, Kuang-Chih Lee and Liangyue Li. Their work appears in journals such as IEEE Access, Engineering Applications of Artificial Intelligence and ACM Transactions on Information Systems.
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.