| Autor: |
Andres Solano-Barliza, Aida Valls, Melisa Acosta-Coll, Antonio Moreno, José Escorcia-Gutierrez, Emiro De-La-Hoz-Franco, Isabel Arregoces-Julio |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-25 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1875-6883 |
| DOI: |
10.1007/s44196-024-00700-8 |
| Popis: |
Abstract This study addresses the problem of recommending restaurants in emerging tourist destinations, taking into account factors vital in these locations, such as location, safety, price and services. The novel recommendation model is based on the well-known logical scoring of preferences (LSP) methodology. The system considers individual preferences across a hierarchy of criteria. The user can customize the recommender by providing suitability scores and aggregation operators for each criterion. The first contribution is the identification of relevant criteria for the selection of restaurants in emerging destinations and the definition of a new scoring system to manage user preferences regarding types of food. The second contribution of this study is the selection of appropriate conjunctive/disjunctive aggregation operators. The recommender system has been tested in a use case in Riohacha (Colombia), obtaining promising results in a wide range of user profiles. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink