Weike Pan
Impact in
- Information Systems top 0.2%
- Recommender Systems and Techniques
- Expert finding and Q&A systems
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks
- Privacy-Preserving Technologies in Data
- Topic Modeling
Papers in
-
- Recommender Systems and Techniques 92
- Expert finding and Q&A systems 14
-
- Advanced Graph Neural Networks 37
- Privacy-Preserving Technologies in Data 15
- Topic Modeling 13
- Data Stream Mining Techniques 7
- Co-authors
- Qiang Yang (21 shared papers)Zhong Ming (36 shared papers)Zhong Ming (46 shared papers)Li Chen (8 shared papers)Evan Wei Xiang (5 shared papers)Feng Liang (3 shared papers)Nathan Liu (1 shared paper)James T. Kwok (3 shared papers)
In The Last Decade
Weike Pan
102 papers receiving 2.5k citations
Peers
Comparison fields: 5 of 100
- Information Systems 1.7k
- Artificial Intelligence 1.5k
- Management Science and Operations Research 496
- Transportation 176
- Computer Vision and Pattern Recognition 536
Countries citing papers authored by Weike Pan
This map shows the geographic impact of Weike Pan'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 Weike Pan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weike Pan more than expected).
Fields of papers citing papers by Weike Pan
This network shows the impact of papers produced by Weike Pan. 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 Weike Pan. The network helps show where Weike Pan may publish in the future.
Co-authors
The 25 scholars most cited alongside Weike Pan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 253 | |
| 2 | 2009 | 148 | |
| 3 | GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering | 2013 | 144 |
| 4 | 2020 | 141 | |
| 5 | 2014 | 112 | |
| 6 | 2020 | 110 | |
| 7 | 2015 | 107 | |
| 8 | 2013 | 103 | |
| 9 | 2011 | 89 | |
| 10 | 2021 | 86 | |
| 11 | Accelerated Gradient Methods for Stochastic Optimization and Online Learning | 2009 | 65 |
| 12 | 2005 | 65 | |
| 13 | 2020 | 54 | |
| 14 | 2021 | 52 | |
| 15 | 2022 | 45 | |
| 16 | 2021 | 41 | |
| 17 | 2013 | 39 | |
| 18 | 2015 | 39 | |
| 19 | 2016 | 38 | |
| 20 | 2013 | 38 |
About Weike Pan
Weike Pan is a scholar working on Information Systems, Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition and Transportation, having authored 111 papers that have together received 2.5k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (92 papers), Advanced Bandit Algorithms Research (41 papers), Advanced Graph Neural Networks (37 papers), Privacy-Preserving Technologies in Data (15 papers), Expert finding and Q&A systems (14 papers), Topic Modeling (13 papers), Human Mobility and Location-Based Analysis (9 papers) and Data Stream Mining Techniques (7 papers). The work is most often cited by research in Information Systems (1.7k citations), Artificial Intelligence (1.5k citations), Management Science and Operations Research (496 citations), Transportation (176 citations) and Computer Vision and Pattern Recognition (536 citations). Weike Pan has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Qiang Yang, Zhong Ming, Zhong Ming, Li Chen, Evan Wei Xiang, Feng Liang, Nathan Liu, James T. Kwok, Congfu Xu and Jaime G. Carbonell. Their work appears in journals such as IEEE Intelligent Systems, Neurocomputing, ACM Transactions on Information Systems, Knowledge-Based Systems and Information Sciences.
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.