Towards automating the construction of recommender systems for low-code development platforms
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Association for Computing MachineryDate
2020-10-16Citation
10.1145/3417990.3420200
The 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS '20). Association for Computing Machinery, 2020. 1–10
ISBN
9781450381352DOI
10.1145/3417990.3420200Funded by
This 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)Project
Gobierno de España. RTI2018-095255-B-I00; Comunidad de Madrid. P2018/TCS-4314Editor's Version
https://doi.org/10.1145/3417990.3420200Subjects
Domain-specific languages; Low-code platform; Model-driven engineering; Recommender system; InformáticaNote
© 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/10.1145/3417990.3420200Rights
© Association for Computring MachineryAbstract
Low-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 context
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Google Scholar:Almonte, Lissette
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Cantador Gutiérrez, Iván
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Guerra Sánchez, Esther
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Lara Jaramillo, Juan de
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