RT Journal Article
T1 Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System
A1 Lara Cabrera, Raúl
A1 González, Álvaro
A1 Ortega Ojeda, Fernando
A1 González Prieto, José Ángel
AB 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.
PB MPDI
SN 2076-3417
YR 2022
FD 2022-01-24
LK https://hdl.handle.net/20.500.14352/72083
UL https://hdl.handle.net/20.500.14352/72083
LA eng
NO 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.
NO Ministerio de Ciencia, Innovación y Universidades (España)/Fondo Europeo de Desarrollo Regional
NO Comunidad de Madrid
DS Docta Complutense
RD 6 oct 2024