Irwin King
Impact in
- Information Systems top 0.01%
- Recommender Systems and Techniques
- Service-Oriented Architecture and Web Services
- Information Retrieval and Search Behavior
- Artificial Intelligence top 0.1%
- Advanced Graph Neural Networks
- Topic Modeling
- Intelligent Tutoring Systems and Adaptive Learning
Papers in
-
- Topic Modeling 45
- Advanced Graph Neural Networks 36
- Machine Learning and Algorithms 24
- Natural Language Processing Techniques 19
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- Recommender Systems and Techniques 76
- Information Retrieval and Search Behavior 34
- Expert finding and Q&A systems 18
- Co-authors
- Michael Rung-Tsong Lyu (209 shared papers)Hao Ma (20 shared papers)Haiqin Yang (44 shared papers)Zenglin Xu (39 shared papers)Zibin Zheng (6 shared papers)Jia-Ni Zhang (6 shared papers)Haixuan Yang (5 shared papers)Zixing Song (20 shared papers)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (11 papers)Neural Networks (7 papers)IEEE Transactions on Neural Networks and Learning Systems (6 papers)Lecture notes in computer science (48 papers)ACM Transactions on Intelligent Systems and Technology (4 papers)
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Irwin King
475 papers receiving 20.4k citations
Irwin King's Hit Papers
Peers
Comparison fields: 5 of 167
- Information Systems 9.7k
- Artificial Intelligence 6.2k
- Computer Science Applications 816
- Computational Mathematics 82
- Computer Vision and Pattern Recognition 2.4k
Countries citing papers authored by Irwin King
This map shows the geographic impact of Irwin King'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 Irwin King with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Irwin King more than expected).
Fields of papers citing papers by Irwin King
This network shows the impact of papers produced by Irwin King. 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 Irwin King. The network helps show where Irwin King may publish in the future.
Co-authors
The 25 scholars most cited alongside Irwin King, 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 500 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Recommender systems with social regularization Hit paper breakdown → | 2011 | 1303 |
| 2 | SoRec Hit paper breakdown → | 2008 | 1180 |
| 3 | Learning to recommend with social trust ensemble Hit paper breakdown → | 2009 | 706 |
| 4 | MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding Hit paper breakdown → | 2020 | 679 |
| 5 | QoS-Aware Web Service Recommendation by Collaborative Filtering Hit paper breakdown → | 2010 | 676 |
| 6 | A Survey on Deep Semi-Supervised Learning Hit paper breakdown → | 2022 | 640 |
| 7 | Dynamic Key-Value Memory Networks for Knowledge Tracing Hit paper breakdown → | 2017 | 557 |
| 8 | Where you like to go next: successive point-of-interest recommendation Hit paper breakdown → | 2013 | 393 |
| 9 | WSRec: A Collaborative Filtering Based Web Service Recommender System Hit paper breakdown → | 2009 | 388 |
| 10 | Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization Hit paper breakdown → | 2012 | 375 |
| 11 | Effective missing data prediction for collaborative filtering Hit paper breakdown → | 2007 | 352 |
| 12 | 2010 | 323 | |
| 13 | Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering Hit paper breakdown → | 2014 | 322 |
| 14 | 2011 | 299 | |
| 15 | Ratings meet reviews, a combined approach to recommend Hit paper breakdown → | 2014 | 294 |
| 16 | Creation and Evaluation of a Pretertiary Artificial Intelligence (AI) Curriculum Hit paper breakdown → | 2021 | 269 |
| 17 | A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Hit paper breakdown → | 2024 | 265 |
| 18 | 2019 | 259 | |
| 19 | Graph-Based Semi-Supervised Learning: A Comprehensive Review Hit paper breakdown → | 2022 | 232 |
| 20 | 2008 | 199 |
About Irwin King
Irwin King is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Science Applications and Statistical and Nonlinear Physics, having authored 500 papers that have together received 21k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (76 papers), Topic Modeling (45 papers), Advanced Graph Neural Networks (36 papers), Information Retrieval and Search Behavior (34 papers), Image Retrieval and Classification Techniques (25 papers), Machine Learning and Algorithms (24 papers), Natural Language Processing Techniques (19 papers) and Expert finding and Q&A systems (18 papers). The work is most often cited by research in Information Systems (9.7k citations), Artificial Intelligence (6.2k citations), Computer Science Applications (816 citations), Computational Mathematics (82 citations) and Computer Vision and Pattern Recognition (2.4k citations). Irwin King has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Michael Rung-Tsong Lyu, Hao Ma, Haiqin Yang, Zenglin Xu, Zibin Zheng, Jia-Ni Zhang, Haixuan Yang, Zixing Song, Chao Liu and Dengyong Zhou. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Lecture notes in computer science and ACM Transactions on Intelligent Systems and Technology.
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.