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A qualitative attraction ranking model for personalized recommendations

A qualitative attraction ranking model for personalized recommendations PurposeThis 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/approachTo 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).FindingsThe 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/implicationsThe 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 implicationsThis 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/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hospitality and Tourism Technology Emerald Publishing

A qualitative attraction ranking model for personalized recommendations

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References (26)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1757-9880
DOI
10.1108/JHTT-09-2016-0047
Publisher site
See Article on Publisher Site

Abstract

PurposeThis 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/approachTo 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).FindingsThe 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/implicationsThe 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 implicationsThis 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/valueThis 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.

Journal

Journal of Hospitality and Tourism TechnologyEmerald Publishing

Published: Mar 12, 2018

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