  Reliability measures associated with the prediction of the machine learning
models are critical to strengthening user confidence in artificial
intelligence. Therefore, those models that are able to provide not only
predictions, but also reliability, enjoy greater popularity. In the field of
recommender systems, reliability is crucial, since users tend to prefer those
recommendations that are sure to interest them, that is, high predictions with
high reliabilities. In this paper, we propose Restricted Bernoulli Matrix
Factorization (ResBeMF), a new algorithm aimed at enhancing the performance of
classification-based collaborative filtering. The proposed model has been
compared to other existing solutions in the literature in terms of prediction
quality (Mean Absolute Error and accuracy scores), prediction quantity
(coverage score) and recommendation quality (Mean Average Precision score). The
experimental results demonstrate that the proposed model provides a good
balance in terms of the quality measures used compared to other recommendation
models.
