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Hybrid group recommendations for a travel service

(2017) MULTIMEDIA TOOLS AND APPLICATIONS. 76(2). p.2787-2811
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Abstract
Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.
Keywords
SYSTEMS, TOURISM, Recommender system, Group recommendations, Travel, Tourism, Hybrid, Collaborative filtering, Content-based recommender

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Citation

Please use this url to cite or link to this publication:

MLA
De Pessemier, Toon, et al. “Hybrid Group Recommendations for a Travel Service.” MULTIMEDIA TOOLS AND APPLICATIONS, vol. 76, no. 2, Springer, 2017, pp. 2787–811, doi:10.1007/s11042-016-3265-x.
APA
De Pessemier, T., Dhondt, J., & Martens, L. (2017). Hybrid group recommendations for a travel service. MULTIMEDIA TOOLS AND APPLICATIONS, 76(2), 2787–2811. https://doi.org/10.1007/s11042-016-3265-x
Chicago author-date
De Pessemier, Toon, Jeroen Dhondt, and Luc Martens. 2017. “Hybrid Group Recommendations for a Travel Service.” MULTIMEDIA TOOLS AND APPLICATIONS 76 (2): 2787–2811. https://doi.org/10.1007/s11042-016-3265-x.
Chicago author-date (all authors)
De Pessemier, Toon, Jeroen Dhondt, and Luc Martens. 2017. “Hybrid Group Recommendations for a Travel Service.” MULTIMEDIA TOOLS AND APPLICATIONS 76 (2): 2787–2811. doi:10.1007/s11042-016-3265-x.
Vancouver
1.
De Pessemier T, Dhondt J, Martens L. Hybrid group recommendations for a travel service. MULTIMEDIA TOOLS AND APPLICATIONS. 2017;76(2):2787–811.
IEEE
[1]
T. De Pessemier, J. Dhondt, and L. Martens, “Hybrid group recommendations for a travel service,” MULTIMEDIA TOOLS AND APPLICATIONS, vol. 76, no. 2, pp. 2787–2811, 2017.
@article{8514419,
  abstract     = {{Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.}},
  author       = {{De Pessemier, Toon and Dhondt, Jeroen and Martens, Luc}},
  issn         = {{1380-7501}},
  journal      = {{MULTIMEDIA TOOLS AND APPLICATIONS}},
  keywords     = {{SYSTEMS,TOURISM,Recommender system,Group recommendations,Travel,Tourism,Hybrid,Collaborative filtering,Content-based recommender}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{2787--2811}},
  publisher    = {{Springer}},
  title        = {{Hybrid group recommendations for a travel service}},
  url          = {{http://doi.org/10.1007/s11042-016-3265-x}},
  volume       = {{76}},
  year         = {{2017}},
}

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