Da Luo
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Advanced Graph Neural Networks
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
- Text and Document Classification Technologies
- Information Systems top 5%
- Recommender Systems and Techniques
Papers in
-
- Topic Modeling 5
- Natural Language Processing Techniques 3
- Sentiment Analysis and Opinion Mining 2
- Advanced Graph Neural Networks 2
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- Recommender Systems and Techniques 4
- Co-authors
- Kangyi Lin (4 shared papers)Chao Huang (3 shared papers)Lianghao Xia (3 shared papers)Yuhao Yang (2 shared papers)Wei Wei (1 shared paper)Yulong Yan (1 shared paper)Kenneth D. Westover (1 shared paper)Dan Nguyen (1 shared paper)
In The Last Decade
Da Luo
12 papers receiving 356 citations
Da Luo's Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 253
- Information Systems 152
- Computer Vision and Pattern Recognition 65
- Signal Processing 30
- Management Science and Operations Research 30
Countries citing papers authored by Da Luo
This map shows the geographic impact of Da Luo'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 Da Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Da Luo more than expected).
Fields of papers citing papers by Da Luo
This network shows the impact of papers produced by Da Luo. 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 Da Luo. The network helps show where Da Luo may publish in the future.
Co-authors
The 22 scholars most cited alongside Da Luo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Debiased Contrastive Learning for Sequential Recommendation Hit paper breakdown → | 2023 | 95 |
| 2 | 2019 | 91 | |
| 3 | 2018 | 50 | |
| 4 | 2020 | 38 | |
| 5 | 2020 | 32 | |
| 6 | 2024 | 24 | |
| 7 | 2024 | 12 | |
| 8 | 2019 | 12 | |
| 9 | 2023 | 7 | |
| 10 | 2019 | 4 | |
| 11 | 2024 | 3 | |
| 12 | 2024 | 2 | |
| 13 | 2026 | 0 |
About Da Luo
Da Luo is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Civil and Structural Engineering, having authored 13 papers that have together received 370 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Recommender Systems and Techniques (4 papers), Natural Language Processing Techniques (3 papers), Sentiment Analysis and Opinion Mining (2 papers), Infrastructure Maintenance and Monitoring (2 papers), Advanced Graph Neural Networks (2 papers), Non-Destructive Testing Techniques (2 papers) and Caching and Content Delivery (2 papers). The work is most often cited by research in Artificial Intelligence (253 citations), Information Systems (152 citations), Computer Vision and Pattern Recognition (65 citations), Signal Processing (30 citations) and Management Science and Operations Research (30 citations). Da Luo has collaborated with scholars based in China, Hong Kong and Japan. Frequent co-authors include Kangyi Lin, Chao Huang, Lianghao Xia, Yuhao Yang, Wei Wei, Yulong Yan, Kenneth D. Westover, Dan Nguyen, Zohaib Iqbal and Troy Long. Their work appears in journals such as IEEE Access, Applied Thermal Engineering, Structures, Measurement and PLoS ONE.
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.