Gangmin Li

1.1k total citations
55 papers, 686 citations indexed

About

Gangmin Li is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Gangmin Li has authored 55 papers receiving a total of 686 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 17 papers in Information Systems and 10 papers in Computer Networks and Communications. Recurrent topics in Gangmin Li's work include Topic Modeling (14 papers), Natural Language Processing Techniques (12 papers) and Recommender Systems and Techniques (8 papers). Gangmin Li is often cited by papers focused on Topic Modeling (14 papers), Natural Language Processing Techniques (12 papers) and Recommender Systems and Techniques (8 papers). Gangmin Li collaborates with scholars based in China, United Kingdom and New Zealand. Gangmin Li's co-authors include Pin Ni, Yuming Li, Victor Chang, Simon Buckingham Shum, Victoria Uren, Xuming Bai, Xutao Wang, Katie Atkinson, Hongfang Yu and Muthu Ramachandran and has published in prestigious journals such as IEEE Access, Applied Soft Computing and Applied Sciences.

In The Last Decade

Gangmin Li

52 papers receiving 639 citations

Peers

Gangmin Li
László Kovács Hungary
Alessio Bechini Italy
Yuanxin Ouyang China
Wei‐Po Lee Taiwan
Mansaf Alam India
Haiyang Yu China
James Michaelis United States
Daochen Zha United States
Hoda K. Mohamed Egypt
László Kovács Hungary
Gangmin Li
Citations per year, relative to Gangmin Li Gangmin Li (= 1×) peers László Kovács

Countries citing papers authored by Gangmin Li

Since Specialization
Citations

This map shows the geographic impact of Gangmin Li'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 Gangmin Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gangmin Li more than expected).

Fields of papers citing papers by Gangmin Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gangmin Li. 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 Gangmin Li. The network helps show where Gangmin Li may publish in the future.

Co-authorship network of co-authors of Gangmin Li

This figure shows the co-authorship network connecting the top 25 collaborators of Gangmin Li. A scholar is included among the top collaborators of Gangmin Li 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 Gangmin Li. Gangmin Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Gangmin, et al.. (2024). Fine-tuning of LLMs for HeXie Management Theory. 69–73.
2.
Yue, Yong, et al.. (2024). Chain-of-thought prompting empowered generative user modeling for personalized recommendation. Neural Computing and Applications. 36(34). 21723–21742. 2 indexed citations
3.
Man, Ka Lok, et al.. (2024). Evaluating and Selecting Deep Reinforcement Learning Models for OptimalDynamic Pricing: A Systematic Comparison of PPO, DDPG, and SAC. University of Bedfordshire Repository (University of Bedfordshire). 215–219. 4 indexed citations
4.
Li, Gangmin, et al.. (2024). Non-Stationary Transformer Architecture: A Versatile Framework for Recommendation Systems. Electronics. 13(11). 2075–2075. 1 indexed citations
5.
Yue, Yong, et al.. (2023). KEMIM: Knowledge-Enhanced User Multi-Interest Modeling for Recommender Systems. IEEE Access. 11. 55425–55434. 5 indexed citations
6.
7.
Yue, Yong, et al.. (2023). Understanding Chinese Moral Stories with Further Pre-Training. 12(2). 1–12.
8.
9.
Li, Gangmin, et al.. (2022). Accurate and Visual Video Recommendation Based on Deep Neural Network. University of Bedfordshire Repository (University of Bedfordshire). 278–283. 2 indexed citations
10.
Qian, Jing, Gangmin Li, Katie Atkinson, & Yong Yue. (2021). Understanding Negative Sampling in Knowledge Graph Embedding. International Journal of Artificial Intelligence & Applications. 12(1). 71–81. 2 indexed citations
11.
Ni, Pin, et al.. (2021). An Encoder-decoder Architecture with Graph Convolutional Networks for Abstractive Summarization. University of Bedfordshire Repository (University of Bedfordshire). 91–97. 2 indexed citations
12.
Ni, Pin, Yuming Li, Gangmin Li, & Victor Chang. (2020). Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interaction. Neural Computing and Applications. 32(20). 16149–16166. 42 indexed citations
13.
Ni, Pin, et al.. (2019). Automatic Generation of Electronic Medical Record Based on GPT2 Model. 6180–6182. 5 indexed citations
14.
Wang, Xutao, et al.. (2019). Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records. 1–5. 113 indexed citations
15.
16.
Li, Gangmin, et al.. (2018). Expert CF: Solving Data Matrix Sparsity and Computation Complexity Problems. Transactions on Machine Learning and Artificial Intelligence. 6(2). 1 indexed citations
17.
Payne, Terry R., et al.. (2017). Dialogue Based Decision Making in Online Trading. Transactions on Machine Learning and Artificial Intelligence. 5(3). 51. 1 indexed citations
18.
Sha, Meng, et al.. (2010). Vocabulary and Language Model Adaptation Using just One File. International Conference on Acoustics, Speech, and Signal Processing. 2 indexed citations
19.
Sha, Meng, et al.. (2010). Vocabulary and language model adaptation using just one speech file. 5410–5413. 8 indexed citations
20.
Kawasaki, Haruhisa & Gangmin Li. (2003). Gain tuning in discrete-time adaptive control for robots. Society of Instrument and Control Engineers of Japan. 2. 1886–1891. 6 indexed citations

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.

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