@inproceedings{
 title = {RecOnto: An ontology to model recommender systems and its components},
 type = {inproceedings},
 year = {2017},
 pages = {815-821},
 publisher = {IEEE},
 id = {61778ad5-1532-328d-8391-ba090ac9a5cd},
 created = {2018-10-19T11:37:38.021Z},
 file_attached = {false},
 profile_id = {b1a06709-e10b-31fa-a43e-738b5ff69456},
 group_id = {6f98721e-fcef-3619-aded-7faa1ef9dce2},
 last_modified = {2018-12-14T12:16:33.529Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Jorro-Aragoneses2017b},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Nowadays, recommender systems are useful tools to
filter items and information for users. There is a huge diversity
of approaches to create customized recommendations. Because of
this, a developer needs to know the features of these approaches
to select which one is the best approach in a specific domain.
In this paper, we explain the first step in the design of our
intelligent framework to create recommender systems. This first
step is called RecOnto: an ontology to model recommender
system as a collection of components related between them. This
ontology defines and classifies all components that compound a
recommender system. Moreover, depending on the information
used by the recommender system, it can filter the components
used by the system. In addition, this ontology can be extended
to add more components or apply this model in other domains.
Finally, we explain an example about how to apply RecOnto to
model CoCARE, a real context-aware recommender system in
the health domain.},
 bibtype = {inproceedings},
 author = {Jorro-Aragoneses, Jose L and D\'\iaz-Agudo, Belén and Recio-Garc\'\ia, Juan A and López-Gutierrez, Diego M and Ceron-Rios, Gineth M},
 doi = {10.1109/ICTAI.2017.00128},
 booktitle = {2017 International Conference on Tools with Artificial Intelligence}
}

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There is a huge diversity\nof approaches to create customized recommendations. Because of\nthis, a developer needs to know the features of these approaches\nto select which one is the best approach in a specific domain.\nIn this paper, we explain the first step in the design of our\nintelligent framework to create recommender systems. This first\nstep is called RecOnto: an ontology to model recommender\nsystem as a collection of components related between them. This\nontology defines and classifies all components that compound a\nrecommender system. Moreover, depending on the information\nused by the recommender system, it can filter the components\nused by the system. In addition, this ontology can be extended\nto add more components or apply this model in other domains.\nFinally, we explain an example about how to apply RecOnto to\nmodel CoCARE, a real context-aware recommender system in\nthe health domain.","bibtype":"inproceedings","author":"Jorro-Aragoneses, Jose L and D\\'\\iaz-Agudo, Belén and Recio-Garc\\'\\ia, Juan A and López-Gutierrez, Diego M and Ceron-Rios, Gineth M","doi":"10.1109/ICTAI.2017.00128","booktitle":"2017 International Conference on Tools with Artificial Intelligence","bibtex":"@inproceedings{\n title = {RecOnto: An ontology to model recommender systems and its components},\n type = {inproceedings},\n year = {2017},\n pages = {815-821},\n publisher = {IEEE},\n id = {61778ad5-1532-328d-8391-ba090ac9a5cd},\n created = {2018-10-19T11:37:38.021Z},\n file_attached = {false},\n profile_id = {b1a06709-e10b-31fa-a43e-738b5ff69456},\n group_id = {6f98721e-fcef-3619-aded-7faa1ef9dce2},\n last_modified = {2018-12-14T12:16:33.529Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jorro-Aragoneses2017b},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Nowadays, recommender systems are useful tools to\nfilter items and information for users. 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