{rfName}
Vi

Indexed in

License and use

Citations

16

Altmetrics

Analysis of institutional authors

Gil, S.AuthorBobadilla, J.Corresponding AuthorOrtega, F.Author

Share

Publications
>
Article

VisualRS: Java framework for visualization of recommender systems information

Publicated to:Knowledge-Based Systems. 155 66-70 - 2018-09-01 155(), DOI: 10.1016/j.knosys.2018.04.028

Authors: Gil, S; Bobadilla, J; Ortega, F; Zhu, B

Affiliations

Univ Politecn Madrid, Beijing Inst Technol, Madrid, Spain - Author
Univ Politecn Madrid, Madrid, Spain - Author
Univ Tecnol & Arte Digital, Las Rozas De Madrid, Spain - Author

Abstract

VisualRS is an object oriented Java framework that allows: 1) To relate items or users from recommender systems, 2) To provide tree graph structures containing the most relevant relationships between items or between users, 3) To create visualizations, using Gephi, of the resulting tree graphs, 4) To select the tree graph structures that allow a more balanced visualization and an easier navigation, 5) To choose between an assortment of collaborative filtering similarity measures, 6) To measure the quality of the results making use of centrality measures in graphs, and 7) To easily embed new similarity metrics and quality measures. VisualRS can be used: 1) As recommender systems analysis tool, 2) To provide users and technical managers with a visualization tool of items and users, and 3) To facilitate research both into the collaborative filtering graphic representation and into the information navigation fields. It provides a broad basis for personalization and experimentation.

Keywords

Collaborative filteringRecommender systemsRelated informationSimilarity measuresTree graphs

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Knowledge-Based Systems due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2018, it was in position 17/134, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.08, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-12, the following number of citations:

  • WoS: 3
  • Scopus: 6
  • Google Scholar: 7
  • OpenCitations: 4

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-12:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 29 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GIL BORRAS, SERGIO) and Last Author (Zhu, B.).

the author responsible for correspondence tasks has been BOBADILLA SANCHO, JESUS.