Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

Luz Santamaria-Granados, Juan Francisco Mendoza-Moreno and Gustavo Ramirez-Gonzalez
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Luz Santamaria-Granados: Facultad de Ingeniería de Sistemas, Universidad Santo Tomás Seccional Tunja, Calle 19 No. 11-64, Tunja, Boyaca 150001, Colombia
Juan Francisco Mendoza-Moreno: Facultad de Ingeniería de Sistemas, Universidad Santo Tomás Seccional Tunja, Calle 19 No. 11-64, Tunja, Boyaca 150001, Colombia
Gustavo Ramirez-Gonzalez: Departamento de Telemática, Universidad del Cauca, Calle 5, No. 4-70, Popayan, Cauca 190002, Colombia

Future Internet, 2020, vol. 13, issue 1, 1-38

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.

Keywords: tourist recommender system; emotion recognition; sentiment analysis; machine learning; deep learning; wearable device; physiological signals (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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