Machine Learning in Tourism

Afsahhosseini, Fatemehalsadat; Al-Mulla, Yaseen A.

doi:10.1145/3426826.3426837

Cited by 15 publications

(11 citation statements)

References 36 publications

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“…Any applications of machine learning in tourism are related to the pre-travel phase, such as: Accommodation (localization, cost, confirmation…), Flight (price, luggage…), Travel planning and so forth [5]. Travel planning is an essential task for anyone who wants to explore new places or take a break from everyday life.…”

Section: Stages Of Travel Before Travelmentioning

confidence: 99%

“…In the context of travel planning, neural networks can be trained on large datasets of travel itineraries to learn patterns and provide travelers with personalized recommendations based on their preferences and past behavior [5]. Overall, using algorithms such as Markov models and neural networks can significantly improve the accuracy and efficiency of itinerary planning RS…”

Section: Stages Of Travel Before Travelmentioning

confidence: 99%

See 1 more Smart Citation

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

BADOUCH¹,

BOUTAOUNTE²

2023

JAIMLNN

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Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recommendation systems (TRS) provide several benefits, including personalization, convenience, improved user experience, and increased revenue for tourism businesses. These systems can suggest destinations, attractions, accommodations, and activities that match user preferences and past behaviors, ultimately simplifying the trip planning process. Machine learning algorithms can be trained on large datasets to generate personalized recommendations, and can continuously improve their effectiveness by incorporating new data and user feedback. This paper provides a state-of-the-art overview of various types of recommendation systems (RS), including those based on user preferences, behaviors, demographic profiles, and social network judgments. The paper also presents a comparison table for these approaches. Additionally, the paper discusses the different stages of the travel process and the sources of data that can be used to develop a recommender system. The concluding section of the paper highlights the importance of personalized recommendations in the tourism industry and the potential for future research in this area.

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Section: Stages Of Travel Before Travelmentioning

confidence: 99%

Section: Stages Of Travel Before Travelmentioning

confidence: 99%

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

BADOUCH¹,

BOUTAOUNTE²

2023

JAIMLNN

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“…The background of the emergence of lifelong learning [1][2][3] under the machine learning [4][5][6] model can be summarized in the following two aspects: First, with the progress of information technology, various types of data are exploding. Secondly, traditional machine learning algorithms are no longer applicable to many application issues in the big data environment, because most traditional machine learning algorithms only focus on classification within a small sample range and lack adaptability to the big data environment.…”

Section: Background: Intelligent Fault Diagnosis Driven By Artificial...mentioning

confidence: 99%

Research on lifelong learning method for intelligent diagnosis of rail transit equipment

Cao

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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With the gradual maturity of statistical machine learning, it is now time to break the traditional model of isolated learning and turn to lifelong learning, pushing machine learning to a new level. Applications such as intelligent assistants, chat robots, and physical robots are also in urgent need of this lifelong learning ability. Without the ability to accumulate knowledge and use it to gradually learn more, a system may never be truly intelligent. Continuous learning algorithms attempt to achieve the same capabilities for neural networks and solve catastrophic forgetting problems. Therefore, in essence, continuous learning performs incremental learning for new tasks.

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“…With the help of sentiment analysis, key characteristics of customer reviews can be analyzed. These characteristics include views on tourist attractions and tourism infrastructure, such as parking, shops, coffee shops, Wi-Fi, and basically any content surrounding the accommodation that can help tourism managers and organizations to improve and therefore attract more customers [5].…”

Section: Introductionmentioning

confidence: 99%

A Smart Tourism Case Study: Classification of Accommodation Using Machine Learning Models Based on Accommodation Characteristics and Online Guest Reviews

2022

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This paper deals with the analysis of data retrieved from a web page for booking accommodation. The main idea of the research is to analyze the relationship between accommodation factors and customer reviews in order to determine the factors that have the greatest influence on customer reviews. Machine learning methods are applied to the collected data and models that can predict the review category for those accommodations that are not evaluated by users are trained. The relationship between certain accommodation factors and classification accuracy of the models is examined in order to get detailed insight into the data used for model training, as well as to make the models more interpretable. The classification accuracy of each model is tested and the precision and recall of the models are examined and compared.

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Machine Learning in Tourism

Cited by 15 publications

References 36 publications

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Research on lifelong learning method for intelligent diagnosis of rail transit equipment

A Smart Tourism Case Study: Classification of Accommodation Using Machine Learning Models Based on Accommodation Characteristics and Online Guest Reviews

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