Chih-Ming Chen

435 total citations
12 papers, 213 citations indexed

About

Chih-Ming Chen is a scholar working on Information Systems, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Chih-Ming Chen has authored 12 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Information Systems, 8 papers in Artificial Intelligence and 6 papers in Signal Processing. Recurrent topics in Chih-Ming Chen's work include Recommender Systems and Techniques (8 papers), Music and Audio Processing (6 papers) and Advanced Graph Neural Networks (6 papers). Chih-Ming Chen is often cited by papers focused on Recommender Systems and Techniques (8 papers), Music and Audio Processing (6 papers) and Advanced Graph Neural Networks (6 papers). Chih-Ming Chen collaborates with scholars based in Taiwan, United States and Austria. Chih-Ming Chen's co-authors include Ming-Feng Tsai, Chuan‐Ju Wang, Jheng-Hong Yang, Yi‐Hsuan Yang, Jen-Yu Liu, Eva Zangerle, Yuan Fang, Ee‐Peng Lim, Yuhan Chen and Han L. Tan and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Affective Computing and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

In The Last Decade

Chih-Ming Chen

10 papers receiving 209 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chih-Ming Chen Taiwan 6 161 153 37 35 34 12 213
Yifan Chen China 10 160 1.0× 146 1.0× 38 1.0× 79 2.3× 26 0.8× 26 246
Jiaying Peng China 4 168 1.0× 152 1.0× 11 0.3× 17 0.5× 50 1.5× 5 226
Chi Thang Duong Australia 9 76 0.5× 173 1.1× 31 0.8× 45 1.3× 22 0.6× 15 252
Fréderic Morin Canada 2 74 0.5× 384 2.5× 27 0.7× 81 2.3× 18 0.5× 2 451
Daniel Beck Australia 8 45 0.3× 304 2.0× 21 0.6× 62 1.8× 14 0.4× 29 365
Vijay Krishnan United States 6 130 0.8× 202 1.3× 19 0.5× 11 0.3× 33 1.0× 10 271
Mohit Kumar India 3 80 0.5× 138 0.9× 17 0.5× 28 0.8× 36 1.1× 5 228

Countries citing papers authored by Chih-Ming Chen

Since Specialization
Citations

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

Fields of papers citing papers by Chih-Ming Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chih-Ming Chen. 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 Chih-Ming Chen. The network helps show where Chih-Ming Chen may publish in the future.

Co-authorship network of co-authors of Chih-Ming Chen

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

All Works

12 of 12 papers shown
1.
Chen, Chih-Ming, et al.. (2025). Rescuing Neurodevelopmental Deficits in AMPA Receptor Gain-of-Function Mutant. bioRxiv (Cold Spring Harbor Laboratory).
2.
Chen, Chih-Ming, et al.. (2022). IPR. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 83. 1912–1916. 4 indexed citations
3.
Chen, Chih-Ming, et al.. (2021). LSTPR: Graph-based Matrix Factorization with Long Short-term Preference Ranking. 2222–2226. 4 indexed citations
4.
Chen, Chih-Ming, et al.. (2020). TPR: Text-aware Preference Ranking for Recommender Systems. 32. 215–224. 4 indexed citations
5.
Chen, Chih-Ming, et al.. (2020). Item Concept Network: Towards Concept-Based Item Representation Learning. IEEE Transactions on Knowledge and Data Engineering. 34(3). 1258–1274.
6.
Zangerle, Eva, Chih-Ming Chen, Ming-Feng Tsai, & Yi‐Hsuan Yang. (2018). Leveraging Affective Hashtags for Ranking Music Recommendations. IEEE Transactions on Affective Computing. 12(1). 78–91. 17 indexed citations
7.
Yang, Jheng-Hong, Chih-Ming Chen, Chuan‐Ju Wang, & Ming-Feng Tsai. (2018). HOP-rec. 140–144. 126 indexed citations
8.
Chen, Chih-Ming, et al.. (2016). Query-based Music Recommendations via Preference Embedding. 79–82. 29 indexed citations
9.
Chen, Chih-Ming, et al.. (2015). Exploiting Latent Social Listening Representations for Music Recommendations. Conference on Recommender Systems. 5 indexed citations
10.
Chen, Chih-Ming, et al.. (2014). Leverage Item Popularity and Recommendation Quality via Cost-Sensitive Factorization Machines. 1158–1162. 1 indexed citations
11.
Chen, Chih-Ming, Ming-Feng Tsai, Jen-Yu Liu, & Yi‐Hsuan Yang. (2013). Using emotional context from article for contextual music recommendation. 649–652. 14 indexed citations
12.
Chen, Chih-Ming, Ming-Feng Tsai, Jen-Yu Liu, & Yi‐Hsuan Yang. (2013). Music Recommendation Based on Multiple Contextual Similarity Information. 65–72. 9 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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