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Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

Authors :
Luz Santamaria-Granados
Juan Francisco Mendoza-Moreno
Gustavo Ramirez-Gonzalez
Source :
Future Internet, Vol 13, Iss 1, p 2 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders.

Details

Language :
English
ISSN :
19995903
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Future Internet
Publication Type :
Academic Journal
Accession number :
edsdoj.377291b9a1674975a9814c9c1a1796ef
Document Type :
article
Full Text :
https://doi.org/10.3390/fi13010002