An Agent Framework of Tourism Recommender System

Jia, Zhi Yang; Gao, Wei; Shi, Yi Jin

doi:10.1051/matecconf/20164401005

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…As we showed in this article, the combination of RSs and SNs is obtaining better results and, indirectly, enhancing the tourism sector's economy (Jia et al, 2016). It is crucial, since the application of RSs in such a customer sensitive sector has become a necessity, not a luxury; moreover, RSs have great value because they assist all parts of the tourism value chain.…”

Section: Discussionmentioning

confidence: 88%

Recommendation Systems for Tourism Based on Social Networks: A Survey

Menk¹,

Sebastiá²,

Ferreira³

2019

Preprint

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Nowadays, recommender systems are present in many daily activities such as online shopping, browsing social networks, etc. Given the rising demand for reinvigoration of the tourist industry through information technology, recommenders have been included into tourism websites such as Expedia, Booking or Tripadvisor, among others. Furthermore, the amount of scientific papers related to recommender systems for tourism is on solid and continuous growth since 2004. Much of this growth is due to social networks that, besides to offer researchers the possibility of using a great mass of available and constantly updated data, they also enable the recommendation systems to become more personalised, effective and natural. This paper reviews and analyses many research publications focusing on tourism recommender systems that use social networks in their projects. We detail their main characteristics, like which social networks are exploited, which data is extracted, the applied recommendation techniques, the methods of evaluation, etc. Through a comprehensive literature review, we aim to collaborate with the future recommender systems, by giving some clear classifications and descriptions of the current tourism recommender systems.

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Section: Discussionmentioning

confidence: 88%

Recommendation Systems for Tourism Based on Social Networks: A Survey

Menk¹,

Sebastiá²,

Ferreira³

2019

Preprint

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“…In the last few years, a growing body of research has been using user-generated content for tourism research. Those studies are focusing on tourist perception of a destination (Hauthal and Burghardt, 2016); destination image (Li et al, 2015;Stepchenkova and Zhan, 2013); destination management and marketing development (Marine-Roig and Clavé, 2015;Sung et al, 2016); organizational reputation (Baka, 2016); tourist behavior and purchase intentions (Alcázar et al, 2014;Cezar and Ö güt, 2012;Del et al, 2015;Herrero et al, 2015;Jin and Phua, 2015;Marchiori and Cantoni, 2015;Tham et al, 2013;Ye et al, 2011Ye et al, , 2009Zhao et al, 2015); understanding collaborative sharing practices (Bilgihan et al, 2016;Munar and Jacobsen, 2014), and the impact of information sharing on education of tourists (Guo et al, 2015;Liu and Park, 2015). Most of the studies are using semantic analysis of information shared by tourists.…”

Section: Mapping Of Visitor Movementsmentioning

confidence: 99%

Geographical mapping of visitor flow in tourism

2018

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The available technology enables us to access a large amount of data shared by tourists on tourism web platforms. Such data include the exact geographical location visited, the time of a visit, and the identifier of a visitor. This article aims to identify the visitor flows in the North East Adriatic region. Visitor flows are groups of repetitive movements of visitors through the geographical space within a certain travel. We identified 31 groups of strategic visitor flows between 188 destinations in the region. The proposed methodological approach is unique and had not been used in this context before. By connecting new approaches in destination management and economic planning, we aim to improve the theoretical and practical knowledge in this field.

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“…The memory-based technique (used in several studies; Ghazarian and Nematbakhsh, 2015; Jia et al , 2016; Petrevska and Koceski, 2012; Sarwar et al ., 2010) uses the entire traveler-attraction database to suggest attractions. There are two methods of this technique.…”

Section: Literature Reviewmentioning

confidence: 99%

A qualitative attraction ranking model for personalized recommendations

Angskun

2018

JHTT

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Purpose This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences. Design/methodology/approach To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP). Findings The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level. Research limitations/implications The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency. Practical implications This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy. Originality/value This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

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An Agent Framework of Tourism Recommender System

Cited by 9 publications

References 17 publications

Recommendation Systems for Tourism Based on Social Networks: A Survey

Recommendation Systems for Tourism Based on Social Networks: A Survey

Geographical mapping of visitor flow in tourism

A qualitative attraction ranking model for personalized recommendations

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