Jorge Castro

423 total citations
11 papers, 276 citations indexed

About

Jorge Castro is a scholar working on Information Systems, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Jorge Castro has authored 11 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Information Systems, 8 papers in Management Science and Operations Research and 6 papers in Artificial Intelligence. Recurrent topics in Jorge Castro's work include Recommender Systems and Techniques (9 papers), Multi-Criteria Decision Making (6 papers) and Text and Document Classification Technologies (3 papers). Jorge Castro is often cited by papers focused on Recommender Systems and Techniques (9 papers), Multi-Criteria Decision Making (6 papers) and Text and Document Classification Technologies (3 papers). Jorge Castro collaborates with scholars based in Spain, Australia and Cuba. Jorge Castro's co-authors include Luis Martı́nez, Raciel Yera, Jie Lü, Guangquan Zhang, Yucheng Dong, Iván Palomares, Manuel J. Barranco, Rosa M. Rodríguez, Álvaro Labella and Héctor González‐Huízar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Applied Soft Computing.

In The Last Decade

Jorge Castro

11 papers receiving 271 citations

Peers

Jorge Castro

IS

AI

MSOR

CVPR

SNP

Jiankai Sun United States

Armelle Brun France

Aghiles Salah Singapore

Xuezhi Cao China

Anne Boyer France

Benjamin Kille Germany

Miguel A. Rueda-Morales Spain

Paolo Tomeo Italy

Chonggang Song China

P. Dolan United States

Jiankai Sun United States

Jorge Castro

175 ×0.8

IS

149 ×1.4

AI

46 ×0.7

MSOR

33 ×0.6

CVPR

37 ×0.8

SNP

Citations per year, relative to Jorge Castro Jorge Castro (= 1×) peers Jiankai Sun

Countries citing papers authored by Jorge Castro

Since Specialization

Citations

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

Fields of papers citing papers by Jorge Castro

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jorge Castro

This figure shows the co-authorship network connecting the top 25 collaborators of Jorge Castro. A scholar is included among the top collaborators of Jorge Castro 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 Jorge Castro. Jorge Castro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Castro, Jorge, et al.. (2024). Single Station Seismic Event Location With a Neural‐Guided Mixture Model: Model‐Based Weighting for Improved Accuracy. SHILAP Revista de lepidopterología. 1(4). 1 indexed citations

2.

Yera, Raciel, Álvaro Labella, Jorge Castro, & Luis Martı́nez. (2018). On group recommendation supported by a minimum cost consensus model. 3 indexed citations

3.

Castro, Jorge, Manuel J. Barranco, Rosa M. Rodríguez, & Luis Martı́nez. (2017). Group Recommendations Based on Hesitant Fuzzy Sets. International Journal of Intelligent Systems. 33(10). 2058–2077. 16 indexed citations

4.

Castro, Jorge, Manuel J. Barranco, Rosa M. Rodríguez, & Luis Martı́nez. (2017). Dealing with diversity and novelty in group recommendations using Hesitant fuzzy sets. 1–6. 2 indexed citations

5.

Castro, Jorge, Jie Lü, Guangquan Zhang, Yucheng Dong, & Luis Martı́nez. (2017). Opinion Dynamics-Based Group Recommender Systems. IEEE Transactions on Systems Man and Cybernetics Systems. 48(12). 2394–2406. 74 indexed citations

6.

Castro, Jorge, Raciel Yera, & Luis Martı́nez. (2017). A fuzzy approach for natural noise management in group recommender systems. Expert Systems with Applications. 94. 237–249. 26 indexed citations

7.

Castro, Jorge, Raciel Yera, & Luis Martı́nez. (2016). An empirical study of natural noise management in group recommendation systems. Decision Support Systems. 94. 1–11. 35 indexed citations

8.

Yera, Raciel, Jorge Castro, & Luis Martı́nez. (2015). A fuzzy model for managing natural noise in recommender systems. Applied Soft Computing. 40. 187–198. 50 indexed citations

9.

Castro, Jorge, et al.. (2015). A Consensus-Driven Group Recommender System. International Journal of Intelligent Systems. 30(8). 887–906. 50 indexed citations

10.

Castro, Jorge, Manuel J. Barranco, & Luis Martı́nez. (2014). IMPROVING GROUP RECOMMENDATION WITH OUTLIER DATA FILTERING. 562–567. 1 indexed citations

11.

Castro, Jorge, Rosa M. Rodríguez, & Manuel J. Barranco. (2013). Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures. International Journal of Computational Intelligence Systems. 7(1). 80–80. 18 indexed citations

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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