Brian Starr
Impact in
- Human-Computer Interaction top 2%
- Usability and User Interface Design
- Innovative Human-Technology Interaction
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- Personal Information Management and User Behavior
Papers in
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- Recommender Systems and Techniques 3
- Web Data Mining and Analysis 2
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- Multimedia Communication and Technology 3
- Co-authors
- Mark S. Ackerman (8 shared papers)Debby Hindus (2 shared papers)Scott Mainwaring (2 shared papers)Michael J. Pazzani (4 shared papers)Dong Joon Kim (1 shared paper)Jack Muramatsu (1 shared paper)P Yap (1 shared paper)Scott Gaffney (1 shared paper)
- Journals
- AI Magazine (1 paper)Computer (1 paper)ACM Transactions on Computer-Human Interaction (1 paper)
- Partner nations
- United States
In The Last Decade
Brian Starr
8 papers receiving 287 citations
Peers
Comparison fields: 5 of 49
- Human-Computer Interaction 152
- Information Systems and Management 76
- Communication 50
- Computer Science Applications 24
- Information Systems 97
Countries citing papers authored by Brian Starr
This map shows the geographic impact of Brian Starr'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 Brian Starr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Starr more than expected).
Fields of papers citing papers by Brian Starr
This network shows the impact of papers produced by Brian Starr. 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 Brian Starr. The network helps show where Brian Starr may publish in the future.
Co-authors
The 9 scholars most cited alongside Brian Starr, 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 | 1997 | 80 | |
| 2 | 1995 | 80 | |
| 3 | 1996 | 68 | |
| 4 | 1996 | 36 | |
| 5 | 1997 | 29 | |
| 6 | 1996 | 22 | |
| 7 | The Do-I-Care agent: effective social discovery and filtering on the web | 1997 | 15 |
| 8 | Do I Care? -- Tell Me What's Changed on the Web | 1996 | 7 |
About Brian Starr
Brian Starr is a scholar working on Information Systems, Sociology and Political Science, Information Systems and Management, Human-Computer Interaction and Computer Networks and Communications, having authored 8 papers that have together received 337 indexed citations. Recurring topics across this work include Multimedia Communication and Technology (3 papers), Personal Information Management and User Behavior (3 papers), Recommender Systems and Techniques (3 papers), Team Dynamics and Performance (2 papers), Web Data Mining and Analysis (2 papers), Usability and User Interface Design (2 papers), Peer-to-Peer Network Technologies (2 papers) and Data Management and Algorithms (1 paper). The work is most often cited by research in Human-Computer Interaction (152 citations), Information Systems and Management (76 citations), Communication (50 citations), Computer Science Applications (24 citations) and Information Systems (97 citations). Brian Starr has collaborated with scholars based in United States. Frequent co-authors include Mark S. Ackerman, Debby Hindus, Scott Mainwaring, Michael J. Pazzani, Dong Joon Kim, Jack Muramatsu, P Yap, Scott Gaffney and Daniel Billsus. Their work appears in journals such as AI Magazine, Computer and ACM Transactions on Computer-Human Interaction.
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.