Facilitating tourists' decision making through open data analyses: A novel recommender system

Pantano, Eleonora; Priporas, Constantinos‐Vasilios; Stylos, Nikolaos; Dennis, Charles

doi:10.1016/j.tmp.2019.06.003

Cited by 14 publications

(5 citation statements)

References 81 publications

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“…Since the 1990s, as industries have become more service-oriented, companies have made it a priority to understand and relate to their consumers more than ever before [1,2]. The tourism industry is no exception [3,4].…”

Section: Introductionmentioning

confidence: 99%

The Moderating Effect of Social Media Use on Sustainable Rural Tourism: A Theory of Planned Behavior Model

2020

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Using the theory of planned behavior (TPB) model, this research aimed to investigate which important factors affect the potential behavior of rural tourism tourists. It also sought to identify the effects of social networking service (SNS) use on the rural tourism using TPB model. For analysis, a partial least squares-structural equation model (PLS-SEM) was used. The results revealed that tourists were influenced by subjective norm and perceived behavior control, of which the effect of subjective norm was greater. Consumers’ use of SNS played a notable role in contributing to the strength of the relationship between intention to visit and subjective norm. Promoters and marketers of sustainable tourism can use the results to make more effective decisions in their businesses.

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Section: Introductionmentioning

confidence: 99%

The Moderating Effect of Social Media Use on Sustainable Rural Tourism: A Theory of Planned Behavior Model

2020

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“…A framework that consists of incremental feature scanning with multiple windows and a hierarchical behavior structure was implemented to associate the multiple modes of the transport system with the recommendation system [25]. A recommender system using a support vector machine was developed to facilitate the tourists by providing support to their decision-making [26]. An item-to-item recommender system based on k-means clustering was discussed in [27] to create image mosaicking.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

et al. 2023

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The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

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“…It can be seen in anthropomorphic robots, chat boxes, smart automated online assistants, hotel concierge, airport welcome staff, museum guides restaurant waters, and online service support (Van Doorn et al, 2017). For instance, the Support Vector Machine was used to model a recommender system for tourists to make a decision (Pantano, Priporas, Stylos, & Dennis, 2019). Naive Bayes, predicted the change in museum visitors with an accuracy of 75.56% (Colladon, Grippa, & Innarella, 2020).…”

Section: Figure3 General View Of Neural Network Layersmentioning

confidence: 99%

Application of Machine Learning in the Hotel Industry: A Critical Review

Alotaibi

2020

Journal of Association of Arab Universities for Tourism and Hos

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Study purpose-The hotel industry like any other industry is witnessing a change due to information and communication technology. However, this change is quite slow. Many researchers in recent time have garnered interest in exploring and implementing the new technologies of artificial intelligence and machine learning in the hotel industry. Therefore, the purpose of this study is to give insights on the role of ML and its integrated technologies in the hotel industry. Design/Methodology/Approach-The study has critically reviewed articles published from 2010 to 2020. To achieve the research objective, the study seeks to answer three main research questions related to the existing literature; RQ1: Where does the hotel industry implement machine learning? RQ2: What are the machine learning techniques used in the hotel industry? RQ3: Which countries are using machine learning in the hotel industry? Findings-The study found that machine learning is helpful in demand forecasting, price forecasting, booking cancellation prediction, financial efficiency, and work efficiency. The machine learning algorithms outperform in the forecast accuracy against the statistical models. The countries at the forefront in machine learning technologies are China and USA. The other countries should take the cue from them and implement machine learning in their hotels Originality of the research-This research conducts exploratory analysis to identify the extent of scientific community knowledge and awareness on machine learning in the hotel industry. To the best of the authors' knowledge, no prior researcher has conducted a similar study specifically in the hotel industry.

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Facilitating tourists' decision making through open data analyses: A novel recommender system

Cited by 14 publications

References 81 publications

The Moderating Effect of Social Media Use on Sustainable Rural Tourism: A Theory of Planned Behavior Model

The Moderating Effect of Social Media Use on Sustainable Rural Tourism: A Theory of Planned Behavior Model

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

Application of Machine Learning in the Hotel Industry: A Critical Review

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