Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1417
Título: A hybrid recommendation approach for a tourism system
Autor: Lucas, Joel P.
Luz, Nuno
Moreno, María
Anacleto, Ricardo
Almeida, Ana
Martins, Constantino
Palavras-chave: Recommender systems
Associative classification
Fuzzy logic
Data: 2013
Editora: Elsevier
Relatório da Série N.º: Expert Systems with Applications; Vol. 40, Issue 9
Resumo: Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.
Peer review: yes
URI: http://hdl.handle.net/10400.22/1417
ISSN: 0957-4174
Versão do Editor: http://www.sciencedirect.com/science/article/pii/S0957417412013024
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