Jack Muramatsu
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
- Web Data Mining and Analysis
- Information Retrieval and Search Behavior
- Artificial Intelligence top 5%
- Semantic Web and Ontologies
- Text and Document Classification Technologies
- Topic Modeling
Papers in
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- Recommender Systems and Techniques 2
- Web Data Mining and Analysis 2
- Information Retrieval and Search Behavior 1
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- Team Dynamics and Performance 2
- Co-authors
- Daniel Billsus (2 shared papers)Michael J. Pazzani (2 shared papers)Wanda Pratt (1 shared paper)Mark S. Ackerman (4 shared papers)David W. McDonald (2 shared papers)Wayne G. Lutters (1 shared paper)Paul Dourish (1 shared paper)Scott Gaffney (1 shared paper)
- Journals
- AI Magazine (1 paper)Computer Supported Cooperative Work (CSCW) (1 paper)National Conference on Artificial Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Jack Muramatsu
8 papers receiving 458 citations
Peers
Comparison fields: 5 of 55
- Information Systems 357
- Artificial Intelligence 273
- Signal Processing 75
- Human-Computer Interaction 35
- Computer Science Applications 26
Countries citing papers authored by Jack Muramatsu
This map shows the geographic impact of Jack Muramatsu'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 Jack Muramatsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack Muramatsu more than expected).
Fields of papers citing papers by Jack Muramatsu
This network shows the impact of papers produced by Jack Muramatsu. 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 Jack Muramatsu. The network helps show where Jack Muramatsu may publish in the future.
Co-authors
The 11 scholars most cited alongside Jack Muramatsu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Syskill & webert: Identifying interesting web sites | 1996 | 384 |
| 2 | 2001 | 68 | |
| 3 | 1998 | 37 | |
| 4 | 1997 | 22 | |
| 5 | Recommenders for Expertise Management | 1999 | 11 |
| 6 | 2010 | 8 | |
| 7 | Social regulation of online multiplayer games | 2004 | 3 |
| 8 | 2003 | 1 |
About Jack Muramatsu
Jack Muramatsu is a scholar working on Information Systems, Social Psychology, Sociology and Political Science, Artificial Intelligence and Computer Science Applications, having authored 8 papers that have together received 534 indexed citations. Recurring topics across this work include Digital Games and Media (3 papers), Team Dynamics and Performance (2 papers), Open Source Software Innovations (2 papers), Recommender Systems and Techniques (2 papers), Web Data Mining and Analysis (2 papers), Information Retrieval and Search Behavior (1 paper), Data Management and Algorithms (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Information Systems (357 citations), Artificial Intelligence (273 citations), Signal Processing (75 citations), Human-Computer Interaction (35 citations) and Computer Science Applications (26 citations). Jack Muramatsu has collaborated with scholars based in United States. Frequent co-authors include Daniel Billsus, Michael J. Pazzani, Wanda Pratt, Mark S. Ackerman, David W. McDonald, Wayne G. Lutters, Paul Dourish, Scott Gaffney, Brian Starr and Dong Joon Kim. Their work appears in journals such as AI Magazine, Computer Supported Cooperative Work (CSCW) and National Conference on Artificial Intelligence.
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.