Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

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.

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

“…Wearables have entered the market in great numbers in recent years [ 9 , 26 ], as have increasingly incorporate sensors that measure physiological signals. The most common sensor present in this type of device is the Photoplethysmogram (PPG) [ 12 , 27 , 28 , 29 ], which registers HR signals.…”

“…Preliminarily, the literature review was conducted to identify the components of the emotion-based tourism recommender frameworks [ 9 ]. This study showed the gap in integrating physiological data from wearable sensors to detect the affective condition of the user as a relevant contextual factor in the satisfaction of the recommendation.…”

“…Wearables of a different range were also used, mainly medium and high-end. Most of these studies took biosignal measurements in controlled experiments [ 9 , 10 , 11 , 12 , 13 ]. As wristbands evolve, they integrate more sensors and with better accuracy.…”

“…In addition, for the TERS engine, two approaches to Content-Based Filtering (CBF) [ 21 , 22 ] and Collaborative Filtering (CF) based on CNN [ 2 , 9 , 23 ] were designed to generate the top-N list of Tourist Experiences (TE) recommendations. The TERS engine integrated a user similarity algorithm, selecting candidate users from the ontology based on the profile and contextual data of the wearable user.…”

OntoTouTra: Tourist Traceability Ontology Based on Big Data Analytics

“…Currently, researchers focus their interest on the development of applications based on ontologies. Such is the case of the scientometric review that we preliminarily carried out on the frameworks of tourist recommendation systems [25] that use heterogeneous data sources extracted from wearable devices, the IoT, social networks, and ontologies. A specific application we found is the TRSO [26] recommendation system for tourists to know the attractions they can see and the activities they can do.…”