OntoTouTra: Tourist Traceability Ontology Based on Big Data Analytics

Mendoza-Moreno, Juan Francisco; Santamaría-Granados, Luz; Fraga, Anabel; Ramírez-González, Gustavo

doi:10.3390/app112211061

Cited by 2 publications

(3 citation statements)

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“…Unlike the sentiment analysis based on the explicit rating of the reviews in the recommendation processes, this study defines a TERS-ER architecture incorporating the contextual data of the users’ emotions before the tourist visit. For this purpose, a knowledge base of tourist destinations from OntoTouTra [ 20 ] is obtained, and the TE are defined according to the AV quadrants. Subsequently, Deep Learning techniques are employed for extracting features and generating the top-N list of TE recommendations.…”

Section: Literature Reviewmentioning

confidence: 99%

“… A dataset of the TE portfolio is consulted from the SPARQL endpoint server. This dataset was acquired from the Ontology of Tourist Traceability (OntoTouTra) proposed in [ 20 ]. A recommender engine that processes MongoDB data collections and TE datasets.…”

Section: System Architecturementioning

confidence: 99%

“…Once the emotion was detected, the Tourist Experience Recommendation System based on the ER (TERS-ER) was developed as the last phase of this study. An interface was designed with the Tourist Traceability Ontology (OntoTouTra) [ 20 ] to get the contextual data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

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Chantre-Astaiza

et al. 2021

Sensors

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The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

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Section: Literature Reviewmentioning

confidence: 99%