Journal of Applied Science and Engineering

Published by Tamkang University Press

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Xiaojun WangThis email address is being protected from spambots. You need JavaScript enabled to view it.

College of Management, Dalian University of Finance and Economics, Dalian, 116622, Liaoning, China


 

 

Received: April 29, 2024
Accepted: June 5, 2024
Publication Date: July 9, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202504_28(4).0020  


Tourism recommendation is a valuable and captivating location-based offering that aids novice travelers in crafting highly personalized travel itineraries. However, existing approaches fall short in capturing the breadth of human preferences and transition patterns. In instances of limited travel data, these methods may even offer recommendations that stray from the genuine travel intentions of tourists. To this end, a multi-granularity contrastive learning within the self-supervised framework is devised for tourism recommendation (MCL-TR), consisting of contrastive POI learning and contrastive tourism learning. Through the joint optimization of dual contrastive learning, MCL-TR achieves a holistic approach to tourism recommendation. By considering both POIs and tourism factors simultaneously, the system can provide comprehensive recommendations that cater to the individual preferences and needs of users, thereby enhancing the overall recommendation quality and user satisfaction. Finally, experiments conducted on three datasets demonstrate that MCL-TR sets a new benchmark in tourism recommendation tasks.


Keywords: Tourism recommendation; multi-granularity contrastive learning, representation learning


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