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dc.contributor.authorBobadilla, Jesús
dc.contributor.authorOrtega, Fernando
dc.contributor.authorGutiérrez, Abraham
dc.contributor.authorAlonso, Santiago
dc.date2020-03
dc.date.accessioned2022-03-24T12:48:31Z
dc.date.available2022-03-24T12:48:31Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12715
dc.description.abstractThis paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 1
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2755es_ES
dc.rightsopenAccesses_ES
dc.subjectrecommendation systemses_ES
dc.subjectclassificationes_ES
dc.subjectneural networkes_ES
dc.subjectcollaborative filteringes_ES
dc.subjectdeep learninges_ES
dc.subjectscalable neural architecturees_ES
dc.subjectIJIMAIes_ES
dc.titleClassification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systemses_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.02.006


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