Pedro G. Campos
- Information Systems top 2%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Management Science and Operations Research top 10%
- Computer Networks and Communications
- Co-authors
- Fernando DíezIván CantadorAlejandro BellogínJosé Antonio Pow-SangManuel Sánchez-MontañésCristhian AguileraTeresa B. LudermirA.F.R. Araujo
- Topics
- Recommender Systems and Techniques (8 papers)Building Energy and Comfort Optimization (2 papers)Solar Radiation and Photovoltaics (2 papers)
In The Last Decade
Pedro G. Campos
26 papers receiving 384 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Information Systems 303
- Artificial Intelligence 150
- Computer Vision and Pattern Recognition 90
- Management Science and Operations Research 83
- Computer Networks and Communications 52
Countries citing papers authored by Pedro G. Campos
This map shows the geographic impact of Pedro G. Campos'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 Pedro G. Campos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pedro G. Campos more than expected).
Fields of papers citing papers by Pedro G. Campos
This network shows the impact of papers produced by Pedro G. Campos. 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 Pedro G. Campos. The network helps show where Pedro G. Campos may publish in the future.
Co-authorship network of co-authors of Pedro G. Campos
This figure shows the co-authorship network connecting the top 25 collaborators of Pedro G. Campos. A scholar is included among the top collaborators of Pedro G. Campos 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 Pedro G. Campos. Pedro G. Campos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 14 | |
| 6 | 6 | |
| 7 | 4 | |
| 8 | 6 | |
| 9 | Translation, cultural adaptation and validation of the self-efficacy to manage chronic disease 6-item scale for european Portuguese. | 7 |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 2 | |
| 13 | Extracting Context Data from User Reviews for Recommendation: A Linked Data Approach. | 1 |
| 14 | 1 | |
| 15 | 1 | |
| 16 | Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocolsbreakdown → | 262 |
| 17 | 7 | |
| 18 | 11 | |
| 19 | 1 | |
| 20 | 1 |
About Pedro G. Campos
Pedro G. Campos is a scholar working on Information Systems, Computer Science Applications and Artificial Intelligence, having authored 31 papers that have together received 406 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (8 papers), Building Energy and Comfort Optimization (2 papers) and Solar Radiation and Photovoltaics (2 papers). The work is most often cited by research in Information Systems (303 citations), Management Science and Operations Research (83 citations) and Transportation (42 citations). Pedro G. Campos has collaborated with scholars based in Chile, Spain and Brazil. Frequent co-authors include Fernando Díez, Iván Cantador, Alejandro Bellogín, José Antonio Pow-Sang, Manuel Sánchez-Montañés, Cristhian Aguilera, Teresa B. Ludermir, A.F.R. Araujo, Gina Tomé and Diego Contreras. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Frontiers in Physiology.
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.