  Beyond accuracy, quality measures are gaining importance in modern
recommender systems, with reliability being one of the most important
indicators in the context of collaborative filtering. This paper proposes
Bernoulli Matrix Factorization (BeMF), which is a matrix factorization model,
to provide both prediction values and reliability values. BeMF is a very
innovative approach from several perspectives: a) it acts on model-based
collaborative filtering rather than on memory-based filtering, b) it does not
use external methods or extended architectures, such as existing solutions, to
provide reliability, c) it is based on a classification-based model instead of
traditional regression-based models, and d) matrix factorization formalism is
supported by the Bernoulli distribution to exploit the binary nature of the
designed classification model. The experimental results show that the more
reliable a prediction is, the less liable it is to be wrong: recommendation
quality improves after the most reliable predictions are selected.
State-of-the-art quality measures for reliability have been tested, which shows
that BeMF outperforms previous baseline methods and models.
