@article{pessemier_hybrid_2016,
	title = {Hybrid group recommendations for a travel service},
	volume = {75},
	issn = {1380-7501},
	url = {http://link.springer.com/article/10.1007/s11042-016-3265-x},
	doi = {10.1007/s11042-016-3265-x},
	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.},
	number = {5},
	urldate = {2016-03-11},
	journal = {Multimed. Tools Appl.},
	author = {Pessemier, Toon De and Dhondt, Jeroen and Martens, Luc},
	month = jan,
	year = {2016},
	pages = {1--25},
}

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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.","number":"5","urldate":"2016-03-11","journal":"Multimed. Tools Appl.","author":[{"propositions":[],"lastnames":["Pessemier"],"firstnames":["Toon","De"],"suffixes":[]},{"propositions":[],"lastnames":["Dhondt"],"firstnames":["Jeroen"],"suffixes":[]},{"propositions":[],"lastnames":["Martens"],"firstnames":["Luc"],"suffixes":[]}],"month":"January","year":"2016","pages":"1–25","bibtex":"@article{pessemier_hybrid_2016,\n\ttitle = {Hybrid group recommendations for a travel service},\n\tvolume = {75},\n\tissn = {1380-7501},\n\turl = {http://link.springer.com/article/10.1007/s11042-016-3265-x},\n\tdoi = {10.1007/s11042-016-3265-x},\n\tabstract = {Recommendation techniques have proven their usefulness as a tool to cope\nwith the information overload problem in many classical domains such as\nmovies, books, and music. Additional challenges for recommender systems\nemerge in the domain of tourism such as acquiring metadata and feedback,\nthe sparsity of the rating matrix, user constraints, and the fact that\ntraveling is often a group activity. This paper proposes a recommender\nsystem that offers personalized recommendations for travel destinations to\nindividuals and groups. These recommendations are based on the users’\nrating profile, personal interests, and specific demands for their next\ndestination. The recommendation algorithm is a hybrid approach combining a\ncontent-based, collaborative filtering, and knowledge-based solution. For\ngroups of users, such as families or friends, individual recommendations\nare aggregated into group recommendations, with an additional opportunity\nfor users to give feedback on these group recommendations. A group of test\nusers evaluated the recommender system using a prototype web application.\nThe results prove the usefulness of individual and group recommendations\nand show that users prefer the hybrid algorithm over each individual\ntechnique. This paper demonstrates the added value of various\nrecommendation algorithms in terms of different quality aspects, compared\nto an unpersonalized list of the most-popular destinations.},\n\tnumber = {5},\n\turldate = {2016-03-11},\n\tjournal = {Multimed. Tools Appl.},\n\tauthor = {Pessemier, Toon De and Dhondt, Jeroen and Martens, Luc},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {1--25},\n}\n\n","author_short":["Pessemier, T. 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