Ruihai Dong

1.6k total citations
61 papers, 830 citations indexed

About

Ruihai Dong is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ruihai Dong has authored 61 papers receiving a total of 830 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 18 papers in Information Systems and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ruihai Dong's work include Topic Modeling (19 papers), Recommender Systems and Techniques (15 papers) and Sentiment Analysis and Opinion Mining (10 papers). Ruihai Dong is often cited by papers focused on Topic Modeling (19 papers), Recommender Systems and Techniques (15 papers) and Sentiment Analysis and Opinion Mining (10 papers). Ruihai Dong collaborates with scholars based in Ireland, China and Japan. Ruihai Dong's co-authors include Barry Smyth, Yichao Lu, Michael P. O’Mahony, Markus Schaal, Linyi Yang, Jian He, Kevin McCarthy, Conrad Childs, Cheng Zhang and John J. Walsh and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and Earth-Science Reviews.

In The Last Decade

Ruihai Dong

52 papers receiving 801 citations

Peers

Ruihai Dong
Yang Xiang China
Bin Zhou China
Sang‐Woon Kim South Korea
S TAN China
Malathi Devarajan India
Mohamed H. Haggag Egypt
Fei Xu China
Ali Rodan Jordan
Mohammed Elbes Jordan
Yang Xiang China
Ruihai Dong
Citations per year, relative to Ruihai Dong Ruihai Dong (= 1×) peers Yang Xiang

Countries citing papers authored by Ruihai Dong

Since Specialization
Citations

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

Fields of papers citing papers by Ruihai Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruihai Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Ruihai Dong. A scholar is included among the top collaborators of Ruihai Dong 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 Ruihai Dong. Ruihai Dong 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
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Wang, Jing, et al.. (2025). AttentionFaultFormer: An attention-enhanced 3D CNN & transformer model for seismic fault detection. Journal of Applied Geophysics. 238. 105707–105707.
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Wang, Xinhui, Jinghang Liu, Ruihai Dong, Michael D. Gilchrist, & Nan Zhang. (2024). High-precision digital light processing (DLP) printing of microstructures for microfluidics applications based on a machine learning approach. Virtual and Physical Prototyping. 19(1). 19 indexed citations
6.
Greene, Derek, et al.. (2024). Topic-Centric Explanations for News Recommendation. 3(2). 1–25. 3 indexed citations
7.
Namee, Brian Mac, et al.. (2024). The Effects of Media Bias on News Recommendations. IEEE Access. 12. 83391–83404.
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Li, Irene, Ruihai Dong, Lei Li, & Li Chen. (2024). EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language Models. 1262–1264.
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Dong, Ruihai, et al.. (2024). A Comparative Study of Vision Transformer and Convolutional Neural Network Models in Geological Fault Detection. IEEE Access. 12. 136148–136159. 5 indexed citations
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Zhang, Cheng, et al.. (2023). Human activity recognition based on multi-modal fusion. 5(3). 321–332. 2 indexed citations
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Wang, Jing, et al.. (2023). Adaptive Adversarial Samples Based Active Learning for Medical Image Classification. 751–758. 2 indexed citations
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Smyth, Barry, et al.. (2023). Stock Embeddings: Representation Learning for Financial Time Series. SHILAP Revista de lepidopterología. 30–30.
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Wang, Qinqin, Ηλίας Τράγος, Neil Hurley, et al.. (2022). Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems. IEEE Access. 10. 71961–71972. 1 indexed citations
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Wang, Qinqin, Ηλίας Τράγος, Neil Hurley, et al.. (2022). Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation. 79–88. 1 indexed citations
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Yang, Linyi, et al.. (2021). Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis. 306–316. 25 indexed citations
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Τράγος, Ηλίας, Makbule Gülçin Özsoy, Ruihai Dong, et al.. (2021). DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning. IEEE Access. 9. 83340–83354. 5 indexed citations
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Jiu-lin, Guo, et al.. (2021). A gigabyte interpreted seismic dataset for automatic fault recognition. SHILAP Revista de lepidopterología. 37. 107219–107219. 14 indexed citations
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Yang, Linyi, et al.. (2020). Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 6150–6160. 35 indexed citations
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Wu, Xiaofei, et al.. (2019). UP-TreeRec: Building dynamic user profiles tree for news recommendation. China Communications. 16(4). 219–233. 7 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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