Jinoh Oh

30 papers receiving 1.1k citations

Jinoh Oh's Hit Papers

Convolutional Matrix Factorization for Document Context-Aware Recommendation 2016 · 523 citations
5230+3+6Years since publication100200300400500

Peers

Jinoh Oh
Comparison fields: 5 of 65
  • Computational Mathematics 42
  • Information Systems 818
  • Artificial Intelligence 663
  • Computer Vision and Pattern Recognition 326
  • Transportation 81
Replace István Pilászy with:
István Pilászy Hungary
Dimitrios Rafailidis Greece
Leandro Balby Marinho Brazil
Dhruv Gupta Germany
Roberto Turrin Italy
Nathan N. Liu Hong Kong
Lejian Liao China
Xiwang Yang United States
Qinyong Wang China
Jinoh Oh relative to István Pilászy Hungary István Pilászy's profile →
Citations per field
00.5×3.7×
István Pilászy · 1×
Citations per year

Countries citing papers authored by Jinoh Oh

Since Specialization
Citations

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

Fields of papers citing papers by Jinoh Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jinoh Oh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jinoh Oh Line = papers co-authored together Jinoh Oh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Convolutional Matrix Factorization for Document Context-Aware Recommendation
Hit paper breakdown →
2016523
2 201190
3 201786
4 201160
5 201649
6 201733
7 201731
8 201531
9 201429
10 201727
11 201724
12 201021
13 201719
14 200913
15 201512
16 201612
17 201311
18 20128
19 20128
20 20097

About Jinoh Oh

Jinoh Oh is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Signal Processing, having authored 30 papers that have together received 1.1k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (12 papers), Advanced Graph Neural Networks (7 papers), Topic Modeling (5 papers), Data Management and Algorithms (4 papers), Image Retrieval and Classification Techniques (4 papers), Caching and Content Delivery (4 papers), Biomedical Text Mining and Ontologies (3 papers) and Complex Network Analysis Techniques (3 papers). The work is most often cited by research in Computational Mathematics (42 citations), Information Systems (818 citations), Artificial Intelligence (663 citations), Computer Vision and Pattern Recognition (326 citations) and Transportation (81 citations). Jinoh Oh has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Hwanjo Yu, Yejin Kim, Chanyoung Park, Sungyoung Lee, Chanyoung Park, Hwanjo Yu, Byoungyoung Lee, Jong Kim, Wook-Shin Han and Sungchul Kim. Their work appears in journals such as Information Sciences, BMC Bioinformatics, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems and Knowledge and Information Systems.

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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