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Lara Cabrera, Raúl González, Álvaro Ortega Ojeda, Fernando González Prieto, José Ángel 2023-06-22T11:04:53Z 2023-06-22T11:04:53Z 2022-01-24 Lara Cabrera, R., González, Á., Ortega Ojeda, F. & González Prieto, J. Á. «Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System». Applied Sciences, vol. 12, n.o 3, enero de 2022, p. 1223. DOI.org (Crossref), https://doi.org/10.3390/app12031223. 2076-3417 10.3390/app12031223 https://hdl.handle.net/20.500.14352/72083 https://doi.org/10.3390/app12031223 https://www.mdpi.com/2076-3417/12/3/1223/htm Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup. eng https://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System journal article