Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System



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Lara Cabrera, Raúl and González, Álvaro and Ortega Ojeda, Fernando and González Prieto, Ángel (2022) Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System. Applied Sciences, 12 (3). p. 1223. ISSN 2076-3417

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Official URL: https://doi.org/10.3390/app12031223


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.

Item Type:Article
Uncontrolled Keywords:recommender systems; collaborative filtering; matrix factorization; reliability; classification model; Dirichlet distribution
Subjects:Sciences > Mathematics > Functional analysis and Operator theory
ID Code:74845
Deposited On:03 Oct 2022 10:54
Last Modified:03 Oct 2022 13:57

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