Quan Lin
- Information Systems top 5%
- Artificial Intelligence top 10%
- Management Science and Operations Research top 5%
- Computational Theory and Mathematics top 5%
- Statistical and Nonlinear Physics top 10%
- Topics
- Probabilistic and Robust Engineering Design (10 papers)Advanced Multi-Objective Optimization Algorithms (9 papers)Recommender Systems and Techniques (6 papers)
- Cited by
- Statistics, Probability and UncertaintyManagement Science and Operations ResearchInformation Systems
- Journals
- International Journal of Biological MacromoleculesKnowledge-Based SystemsStructural and Multidisciplinary Optimization
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Quan Lin
25 papers receiving 437 citations
Peers
Comparison fields: 5 of 81
- Information Systems 184
- Artificial Intelligence 180
- Management Science and Operations Research 105
- Computational Theory and Mathematics 83
- Statistical and Nonlinear Physics 71
Countries citing papers authored by Quan Lin
This map shows the geographic impact of Quan Lin'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 Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quan Lin more than expected).
Fields of papers citing papers by Quan Lin
This network shows the impact of papers produced by Quan Lin. 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 Lin. The network helps show where Quan Lin may publish in the future.
Co-authorship network of co-authors of Quan Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Quan Lin. A scholar is included among the top collaborators of Quan Lin 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 Lin. Quan Lin 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 | 9 | |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 10 | |
| 6 | 17 | |
| 7 | 14 | |
| 8 | 11 | |
| 9 | 17 | |
| 10 | 35 | |
| 11 | 10 | |
| 12 | 8 | |
| 13 | 65 | |
| 14 | 16 | |
| 15 | Conversion Rate Prediction via Post-Click Behaviour Modeling | 2 |
| 16 | 26 | |
| 17 | Multi-Level Deep Cascade Trees for Conversion Rate Prediction. | 1 |
| 18 | 12 | |
| 19 | 4 | |
| 20 | Research of Selecting Stocks Based on Classification Method of SVM | 2 |
About Quan Lin
Quan Lin is a scholar working on Statistics, Probability and Uncertainty, Management Science and Operations Research and Computational Theory and Mathematics, having authored 27 papers that have together received 455 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (10 papers), Advanced Multi-Objective Optimization Algorithms (9 papers) and Recommender Systems and Techniques (6 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (69 citations), Management Science and Operations Research (105 citations) and Information Systems (184 citations). Quan Lin has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Keping Yang, Qi Zhou, Jiexiang Hu, Hong Wen, Fuyu Lv, Wilfred Ng, Fei Sun, Jie Tang, Jimeng Sun and Chenhao Tan. Their work appears in journals such as International Journal of Biological Macromolecules, Knowledge-Based Systems and Structural and Multidisciplinary Optimization.
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.