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Recommendation and Classification Systems: A Systematic Mapping Study
Joint Authors
Domínguez-Mayo, F. J.
Enríquez, J. G.
Morales-Trujillo, L.
Calle-Alonso, Fernando
Lucas-Rodríguez, J. M.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-27
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of increasing their profits or offering a more specialized service to their customers.
Moreover, there are countless applications in which classification algorithms are used, seeking to find patterns that are difficult for people to detect or whose detection cost is very high.
Sometimes, it is necessary to use a mixture of both algorithms to give an optimal solution to a problem.
This is the case of the ADAGIO, a R&D project that combines machine learning (ML) strategies from heterogeneous data sources to generate valuable knowledge based on the available open data.
In order to support the ADAGIO project requirements, the main objective of this paper is to provide a clear vision of the existing classification and recommendation ML systems to help researchers and practitioners to choose the best option.
To achieve this goal, this work presents a systematic review applied in two contexts: scientific and industrial.
More than a thousand papers have been analyzed resulting in 80 primary studies.
Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice.
In fact, the validation presented for both cases is very scarce in the industrial environment.
From the point of view of software development life cycle, this review also shows that the work being done in the ML (for classification and recommendation) research and industrial environment is far from earlier stages such as business requirements and analysis.
This makes it very difficult to find efficient and effective solutions that support real business needs from an early stage.
It is therefore that the article suggests the development of new ML research lines to facilitate its application in the different domains.
American Psychological Association (APA)
Enríquez, J. G.& Morales-Trujillo, L.& Calle-Alonso, Fernando& Domínguez-Mayo, F. J.& Lucas-Rodríguez, J. M.. 2019. Recommendation and Classification Systems: A Systematic Mapping Study. Scientific Programming،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1210761
Modern Language Association (MLA)
Enríquez, J. G.…[et al.]. Recommendation and Classification Systems: A Systematic Mapping Study. Scientific Programming No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1210761
American Medical Association (AMA)
Enríquez, J. G.& Morales-Trujillo, L.& Calle-Alonso, Fernando& Domínguez-Mayo, F. J.& Lucas-Rodríguez, J. M.. Recommendation and Classification Systems: A Systematic Mapping Study. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1210761
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210761