Machine Learning in Tourism: A Brief Overview

Egger, Roman

doi:10.1007/978-3-030-88389-8_6

Cited by 23 publications

(9 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From an academic perspective, and in this “novel visual landscape”, Volo and Irimiás (2021) call for the use of new methods that can analyze the data, first and foremost, photos (available as Big Data), better. However, especially with the emergence of new analytical methods through which users' posts are analyzed via Natural Language Processing or Image Analytics (Egger, 2022), socio-demographic and psychographic characteristics remain completely absent, making it difficult to classify “Instagrammers” in the population. Future research could intertwine the findings from this study with the promising approaches put forth in Ferwerda and Tkalcic (2018) and Jeremy et al (2021) to derive personality traits from Instagram images.…”

Section: Discussionmentioning

confidence: 99%

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Egger

2023

CBTH

Self Cite

View full text Add to dashboard Cite

Purpose This study aims to understand how traveling Instagrammers should be characterized by bridging socio-psychological concepts with Instagram usage and travel typologies. Design/methodology/approach Using analysis of variance, differences between non-Instagrammers and passive and active users were identified with regard to their personality traits and human values; traveling Instagrammers and their travel types were characterized in more detail using k-means clustering, and, finally, posting behavior was examined using multiple regression. Findings The results demonstrate significant differences between the user groups in terms of their personality traits, their human values and their travel behavior. The multiple regressions provide deeper insights into tourists’ Instagram posting. Research limitations/implications This study was conducted on 1,100 German travelers, meaning that the results are only valid for this cultural context. All data were collected using an online panel and participants received incentives, and thus extrinsic rewards, instead of using merely intrinsic motivation to take part in the survey. Originality/value This study contributes to a better understanding of Instagram usage behavior when traveling and adds valuable insights to this field of research. At the same time, the results are relevant for tourism businesses wishing to optimize their communication and marketing strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Egger

2023

CBTH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Strojno učenje od koristi je pri izdvajanju kvalitativnih podataka iz skupova podataka koji su jednostavno preveliki da bi ih ljudi sortirali. Algoritmi strojnog učenja podijeljeni su u dvije glavne kategorije: nadzirano učenje (algoritmi uzimaju ono što je već naučeno i primjenjuju to znanje na novim podacima) i nenadzirano učenje (otkrivanje nepoznatog značenja iz podataka) (Iorio i sur., 2020;Egger, 2022). U kontekstu turizma, strojno učenje može iz online recenzija gostiju izvući razloge za negativne ili pozitivne povratne informacije.…”

Section: Strojno Učenjeunclassified

“…U kontekstu turizma, strojno učenje može iz online recenzija gostiju izvući razloge za negativne ili pozitivne povratne informacije. No prava snaga strojnog učenja leži u predviđanju budućih događanja čak i kada određeni skupovi podataka nedostaju (Egger, 2022), što je krucijalno u stvaranju personaliziranih usluga u hotelijerstvu.…”

Section: Strojno Učenjeunclassified

Personalizacija Usluga Kroz Nove Tehnologije

Mušanović¹,

Šimić²

2022

Hotelska Kuća

View full text Add to dashboard Cite

Svrha – Svrha rada je predstaviti sustav koji uči i unaprjeđuje sam sebe prateći odluke gosta i trendove na tržištu te upoznaje interese, želje i navike gostiju. Obrada teme – Snažan napredak digitalne tehnologije, u posljednjih 25 godina, doveo je do novih paradigmi u poslovanju. Hotelijerstvo je djelatnost u kojoj su disruptivne tehnologije poput umjetne inteligencije, strojnog učenja i virtualnih agenata (chatbotovi) počeli mijenjati poslovanje u obliku obavljanja jednostavnijih zadataka korisničke službe, pružanja jednostavnog iskustva prijave u hotel, upravljanja ‘pametnim’ hotelskim sobama koje se mogu prilagoditi, i predviđanja individualnih zahtjeva gostiju. Zahvaljujući sustavima automatizirane segmentacije hotelijerima je omogućeno bolje razumijevanje interesa i želja gostiju te pružanje visoko personalizirane hotelske usluge jačajući istovremeno prodaju smještajnih kapaciteta te podizanje novčane vrijednosti svakog gosta, ali i same sobe. Zaključci – Sustav automatizirane segmentacije gostiju i personalizacije usluga moguće je implementirati u sve faze putovanja gosta. Pri rezervaciji smještaja, sustavi nude gostima dodatne, primamljive, personalizirane te ne nametljive usluge za nadogradnju sobe u skladu s njihovim interesima. Tijekom boravka se gostima nude usluge kroz različite kanale (na IPTVu, kroz osoblje na recepciji, conciergu, u restoranu, kroz Wi-Fi landing page, kroz sustav SMS poruka, kroz mobilne aplikacije) ovisno o tome koji kanal donosi najveću dobit. Nakon odlaska gosta sustav šalje email zahvale kako bi se iskoristio dojam nedavnog boravka te šalje ponuda za iduću sezonu.

show abstract

“…Researchers have applied machine learning techniques in different fields such as logistics [ 3 , 4 ], tourism [ 5 , 6 ], finance [ 7 ], and so on. In addition, machine learning and deep learning techniques can be adopted to provide effective solutions for the current problem of geofencing.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Movement Monitoring in Clustered Workplaces to Control COVID-19 Based on Geofencing and Fusion of Wi-Fi and Magnetic Field Metrics

El-Haleem

Mohamed²,

Abdelhakam³

et al. 2022

Sensors

View full text Add to dashboard Cite

The ubiquitous existence of COVID-19 has required the management of congested areas such as workplaces. As a result, the use of a variety of inspiring tools to deal with the spread of COVID-19 has been required, including internet of things, artificial intelligence (AI), machine learning (ML), and geofencing technologies. In this work, an efficient approach based on the use of ML and geofencing technology is proposed to monitor and control the density of persons in workplaces during working hours. In particular, the workplace environment is divided into a number of geofences in which each person is associated with a set of geofences that make up their own cluster using a dynamic user-centric clustering scheme. Different metrics are used to generate a unique geofence digital signature (GDS) such as Wi-Fi basic service set identifier, Wi-Fi received signal strength indication, and magnetic field data, which can be collected using the person’s smartphone. Then, these metrics are utilized by different ML techniques to generate the GDS for each indoor geofence and each building geofence as well as to detect whether the person is in their cluster. In addition, a Layered-Architecture Geofence Division method is considered to reduce the processing overhead at the person’s smartphone. Our experimental results demonstrate that the proposed approach can perform well in a real workplace environment. The results show that the system accuracy is about 98.25% in indoor geofences and 76% in building geofences.

show abstract

Machine Learning in Tourism: A Brief Overview

Cited by 23 publications

References 69 publications

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Personalizacija Usluga Kroz Nove Tehnologije

A Machine Learning Approach for Movement Monitoring in Clustered Workplaces to Control COVID-19 Based on Geofencing and Fusion of Wi-Fi and Magnetic Field Metrics

Contact Info

Product

Resources

About