A hybrid recommendation approach for a tourism system
Autor: | Constantino Martins, Ana Maria de Almeida Figueiredo, María N. Moreno, Joel Pinho Lucas, Ricardo Anacleto, Nuno Luz |
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Přispěvatelé: | Repositório Científico do Instituto Politécnico do Porto |
Rok vydání: | 2013 |
Předmět: |
Information retrieval
Association rule learning business.industry Computer science Association (object-oriented programming) Associative classification General Engineering Context (language use) Recommender system Machine learning computer.software_genre Fuzzy logic Computer Science Applications Personalization Artificial Intelligence Order (business) Recommender systems Artificial intelligence business computer Tourism |
Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
ISSN: | 0957-4174 |
DOI: | 10.1016/j.eswa.2012.12.061 |
Popis: | 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. |
Databáze: | OpenAIRE |
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