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oai:docta.ucm.es:20.500.14352/720832024-07-19T15:28:02Zcom_20.500.14352_14col_20.500.14352_15

00925njm 22002777a 4500 dc Lara Cabrera, Raúl author González, Álvaro author Ortega Ojeda, Fernando author González Prieto, José Ángel author 2022-01-24 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. 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 Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System