Hai Liu
- Artificial Intelligence top 5%
- Information Systems top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Computer Science Applications top 5%
- Co-authors
- Zhaoli ZhangXiaoxuan ShenNaixue XiongDuantengchuan LiKe LinBaolin YiJiangbo ShuJiazhang Wang
- Topics
- Recommender Systems and Techniques (7 papers)Image Retrieval and Classification Techniques (3 papers)Advanced Graph Neural Networks (3 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Hai Liu
17 papers receiving 696 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Artificial Intelligence 372
- Information Systems 312
- Computer Vision and Pattern Recognition 214
- Computer Networks and Communications 63
- Computer Science Applications 63
Countries citing papers authored by Hai Liu
This map shows the geographic impact of Hai Liu'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 Hai Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai Liu more than expected).
Fields of papers citing papers by Hai Liu
This network shows the impact of papers produced by Hai Liu. 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 Hai Liu. The network helps show where Hai Liu may publish in the future.
Co-authorship network of co-authors of Hai Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Hai Liu. A scholar is included among the top collaborators of Hai Liu 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 Hai Liu. Hai Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 7 | |
| 3 | 23 | |
| 4 | EDMF: Efficient Deep Matrix Factorization With Review Feature Learning for Industrial Recommender Systembreakdown → | 178 |
| 5 | 107 | |
| 6 | 68 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | 24 | |
| 10 | 63 | |
| 11 | 4 | |
| 12 | 17 | |
| 13 | 159 | |
| 14 | 1 | |
| 15 | 43 | |
| 16 | A Distributed Rational Secret Sharing Scheme with Hybrid Preference Model | 1 |
| 17 | Access Control in Very Loosely Structured Data Model Using Relational Databases | 3 |
| 18 | 6 |
About Hai Liu
Hai Liu is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 18 papers that have together received 717 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (7 papers), Image Retrieval and Classification Techniques (3 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Information Systems (312 citations), Computer Science Applications (63 citations) and Artificial Intelligence (372 citations). Hai Liu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Zhaoli Zhang, Xiaoxuan Shen, Naixue Xiong, Duantengchuan Li, Ke Lin, Baolin Yi, Jiangbo Shu, Jiazhang Wang, Zhen Zhang and Zhifei Li. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Sensors.
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.