RecOnto: An Ontology to Model Recommender Systems and its Components

Jorro-Aragoneses, Jose L.; Cerón-Rios, Gineth; Diaz-Agudo, Ma Belen; Recio-Garcí­a, Juan A.; Lopez-Gutierrez, Diego M.

doi:10.1109/ictai.2017.00128

Cited by 4 publications

(1 citation statement)

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“…In our previous research, we have validated such approaches to generate recommender systems according to the actors (researchers, developers, etc. ), their backgrounds, motivations and goals (Recio-García, González-Calero, & Díaz-Agudo, 2014b;Jorro-Aragoneses, Ceron-Rios, Díaz-Agudo, Recio-García, & López-Gutierrez, 2017). This development process relies on semantic descriptions of work-flows and components using an ontology, RecOnto 1 , which supports the reasoning regarding the correctness of the recommender system being generated.…”

Section: Introductionmentioning

confidence: 99%

Conceptual Modeling of Explainable Recommender Systems: An Ontological Formalization to Guide Their Design and Development

Caro-Martínez

Jiménez-Díaz

Recio-García³

2021

jair

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With the increasing importance of e-commerce and the immense variety of products, users need help to decide which ones are the most interesting to them. This is one of the main goals of recommender systems. However, users’ trust may be compromised if they do not understand how or why the recommendation was achieved. Here, explanations are essential to improve user confidence in recommender systems and to make the recommendation useful. Providing explanation capabilities into recommender systems is not an easy task as their success depends on several aspects such as the explanation’s goal, the user’s expectation, the knowledge available, or the presentation method. Therefore, this work proposes a conceptual model to alleviate this problem by defining the requirements of explanations for recommender systems. Our goal is to provide a model that guides the development of effective explanations for recommender systems as they are correctly designed and suited to the user’s needs. Although earlier explanation taxonomies sustain this work, our model includes new concepts not considered in previous works. Moreover, we make a novel contribution regarding the formalization of this model as an ontology that can be integrated into the development of proper explanations for recommender systems.

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Section: Introductionmentioning

confidence: 99%