Ricardo Kawase
- Computer Science Applications top 1%
- Artificial Intelligence top 5%
- Information Systems top 5%
- Sociology and Political Science
- Management Science and Operations Research top 10%
- Co-authors
- Stefan DietzeUjwal GadirajuGianluca DemartiniPatrick SiehndelEelco HerderBernardo Pereira NunesBesnik FetahuWolfgang Nejdl
- Topics
- Web Data Mining and Analysis (10 papers)Recommender Systems and Techniques (8 papers)Mobile Crowdsensing and Crowdsourcing (7 papers)
In The Last Decade
Ricardo Kawase
37 papers receiving 468 citations
Peers
Comparison fields: 5 of 66
- Computer Science Applications 274
- Artificial Intelligence 230
- Information Systems 130
- Sociology and Political Science 82
- Management Science and Operations Research 66
Countries citing papers authored by Ricardo Kawase
This map shows the geographic impact of Ricardo Kawase'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 Ricardo Kawase with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ricardo Kawase more than expected).
Fields of papers citing papers by Ricardo Kawase
This network shows the impact of papers produced by Ricardo Kawase. 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 Ricardo Kawase. The network helps show where Ricardo Kawase may publish in the future.
Co-authorship network of co-authors of Ricardo Kawase
This figure shows the co-authorship network connecting the top 25 collaborators of Ricardo Kawase. A scholar is included among the top collaborators of Ricardo Kawase 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 Ricardo Kawase. Ricardo Kawase is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 3 | |
| 3 | 145 | |
| 4 | Extracting architectural patterns from web data | 1 |
| 5 | 2 | |
| 6 | 83 | |
| 7 | 3 | |
| 8 | 1 | |
| 9 | 17 | |
| 10 | 2 | |
| 11 | 10 | |
| 12 | 6 | |
| 13 | 3 | |
| 14 | TwikiMe!: user profiles that make sense | 17 |
| 15 | 1 | |
| 16 | 13 | |
| 17 | 4 | |
| 18 | How Predictable Are You? A Comparison of Prediction Algorithms for Web Page Revisitation. | 3 |
| 19 | 5 | |
| 20 | RESEARCH ARTICLE Leveraging Search and Content Exploration by Exploiting Context in Folksonomy Systems | 0 |
About Ricardo Kawase
Ricardo Kawase is a scholar working on Computer Science Applications, Communication and Artificial Intelligence, having authored 38 papers that have together received 480 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (10 papers), Recommender Systems and Techniques (8 papers) and Mobile Crowdsensing and Crowdsourcing (7 papers). The work is most often cited by research in Computer Science Applications (274 citations), Information Systems and Management (62 citations) and Artificial Intelligence (230 citations). Ricardo Kawase has collaborated with scholars based in Germany, Brazil and Greece. Frequent co-authors include Stefan Dietze, Ujwal Gadiraju, Gianluca Demartini, Patrick Siehndel, Eelco Herder, Bernardo Pereira Nunes, Besnik Fetahu, Wolfgang Nejdl, George Papadakis and Marco A. Casanova. Their work appears in journals such as ACM Transactions on Computer-Human Interaction, IEEE Intelligent Systems and Lecture notes in computer science.
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.