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dc.contributor.authorChatterjee, Ayan
dc.contributor.authorPrinz, Andreas
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorMeena, Yogesh Kumar
dc.identifier.citationChatterjee, A., Prinz, A., Riegler, M. A. & Meena, Y. K. (2023). An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology. Scientific Reports, 13 (1).en_US
dc.description.abstractElectronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper introduces a novel approach that combines deep learning and semantic ontologies to generate hybrid and personalized recommendations by considering “Physical Activity” as a case study. To achieve this, we employ three methods: time-series forecasting, time-series physical activity level classification, and statistical metrics for data processing. Additionally, we utilize a naïve-based probabilistic interval prediction technique with the residual standard deviation used to make point predictions meaningful in the recommendation presentation. The processed results are integrated into activity datasets using an ontology called OntoeCoach, which facilitates semantic representation and reasoning. To generate personalized recommendations in an understandable format, we implement the SPARQL Protocol and RDF Query Language (SPARQL). We evaluate the performance of standard time-series forecasting algorithms [such as 1D Convolutional Neural Network Model (CNN1D), autoregression, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)] and classifiers [including Multilayer Perceptron (MLP), Rocket, MiniRocket, and MiniRocketVoting] using state-of-the-art metrics. We conduct evaluations on both public datasets (e.g., PMData) and private datasets (e.g., MOX2-5 activity). Our CNN1D model achieves the highest prediction accuracy of 97, while the MLP model outperforms other classifiers with an accuracy of 74. Furthermore, we evaluate the performance of our proposed OntoeCoach ontology model by assessing reasoning and query execution time metrics. The results demonstrate that our approach effectively plans and generates recommendations on both datasets. The rule set of OntoeCoach can also be generalized to enhance interpretability.en_US
dc.publisherNature Portfolioen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAn automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontologyen_US
dc.title.alternativeAn automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontologyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2023 The Author(s)en_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400en_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.journalScientific Reportsen_US

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