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dc.contributor.authorAlmonte, Lissettees_ES
dc.contributor.authorCantador Gutiérrez, Iván es_ES
dc.contributor.authorGuerra Sánchez, Esther es_ES
dc.contributor.authorLara Jaramillo, Juan de es_ES
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.date.accessioned2022-07-11T08:05:58Zen_US
dc.date.available2022-07-11T08:05:58Zen_US
dc.date.issued2020-10-16en_US
dc.identifier.citationThe 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS '20). Association for Computing Machinery, 2020. 1–10en_US
dc.identifier.isbn9781450381352es_ES
dc.identifier.urihttp://hdl.handle.net/10486/703047en_US
dc.description© ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in {Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, https://doi.org/10.1145/3417990.3420200en_US
dc.description.abstractLow-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations. To alleviate these problems, in this paper, we present the first steps towards a generic model-driven framework capable of generating ad-hoc, task-oriented recommender systems for their integration on low-code platforms. As a proof of concept, we present some preliminary results obtained from an offline evaluation of our framework on three datasets of class diagrams. The results show that the proposed framework is capable of providing relevant recommendations in the given contexten_US
dc.description.sponsorshipThis project has received funding from the EU Horizon 2020 research and innovation programme under the Marie SkłodowskaCurie grant agreement No 813884, the Spanish Ministry of Science (RTI2018-095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314)en_US
dc.format.extent11 pag.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedingsen_US
dc.rights© Association for Computring Machineryen_US
dc.subject.otherDomain-specific languagesen_US
dc.subject.otherLow-code platformen_US
dc.subject.otherModel-driven engineeringen_US
dc.subject.otherRecommender systemen_US
dc.titleTowards automating the construction of recommender systems for low-code development platformsen_US
dc.typebookParten_US
dc.typeconferenceObjecten_US
dc.subject.ecienciaInformáticaes_ES
dc.relation.publisherversionhttps://doi.org/10.1145/3417990.3420200es_ES
dc.identifier.doi10.1145/3417990.3420200en_US
dc.identifier.publicationfirstpage66-1es_ES
dc.identifier.publicationlastpage66-10es_ES
dc.relation.projectIDGobierno de España. RTI2018-095255-B-I00es_ES
dc.relation.projectIDComunidad de Madrid. P2018/TCS-4314es_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen_US
dc.rights.accessRightsopenAccessen_US
dc.facultadUAMEscuela Politécnica Superiores_ES


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