{rfName}
A

Indexed in

License and Use

Icono OpenAccess

Citations

252

Altmetrics

Analysis of institutional authors

F. OrtegaAuthorR. Lara-CabreraAuthor

Share

August 6, 2019
Publications
>
Article
Sí

A Collaborative Filtering approach based on Naive Bayes Classifier

Publicated to: IEEE Access. 7 108581-108592 - 2019-01-01 7(), DOI: 10.1109/ACCESS.2019.2933048

Authors:

Valdiviezo-Diaz, Priscila; Ortega, Fernando; Cobos, Eduardo; Lara-Cabrera, Raul
[+]

Affiliations

Dpto. Sistemas Informáticos, ETSI Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain. - Author
Ingenio Labs, Madrid 28001, Spain - Author
Ingenio Labs, Madrid, Spain. - Author
Univ Politecn Madrid, ETSI Sistemas Informat, Dept Lenguajes & Sistemas Informat, Madrid, Spain - Author
Univ Tecn Particular Loja, Comp Sci & Elect Dept, Loja 1101608, Ecuador - Author
Universidad Técnica Particular de Loja. Computer Science and Electronic Department, Loja, Ecuador and Dpto. Sistemas Informáticos, ETSI Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain. - Author
See more

Abstract

Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users’ and items’ information, respectively. Experiments carried out using four datasets present good results compared to several state-of-the-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction’s accuracy in some datasets.
[+]

Keywords

CollaborationCollaborative filteringData modelsHybrid cfMathematical modelNaive bayes classifierPredictive modelsRecommender systemsReliabilityReliability measure

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access 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, 2019, it was in position 35/156, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.73. This 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: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 4.92 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-08, the following number of citations:

  • WoS: 55
  • Scopus: 96
  • Google Scholar: 101
[+]

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 2026-04-08:

  • 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: 185 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/79085/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 150
  • Downloads: 624
[+]

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Ecuador.

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 (P. Valdiviezo-Diaz) and Last Author (LARA CABRERA, RAUL).

the author responsible for correspondence tasks has been P. Valdiviezo-Diaz.

[+]