02063nas a2200265   4500000000100000000000100001008004100002260001200043653002700055653001900082653001900101653002800120653001800148653003300166100002100199700002000220700002300240700002000263245010600283856009900389300001000488490000600498520127900504022001401783       2020                            d  c03/202010aRecommendation Systems10aClassification10aNeural Network10aCollaborative Filtering10aDeep Learning10aScalable Neural Architecture1 aJesús Bobadilla1 aFernando Ortega1 aAbraham Gutiérrez1 aSantiago Alonso00aClassification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems  uhttps://www.ijimai.org/journal/sites/default/files/files/2020/02/ijimai20206_1_8_pdf_28842.pdf  a68-770 v63 aThis 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.  a1989-1660