Providing reliability in Recommender Systems through Bernoulli Matrix Factorization

TL;DR

This paper introduces Bernoulli Matrix Factorization (BeMF), a novel model-based collaborative filtering approach that provides both predictions and reliability estimates, improving recommendation quality by selecting more reliable predictions.

Contribution

BeMF is the first to integrate Bernoulli distribution into matrix factorization for reliability estimation without external methods or extended architectures.

Findings

BeMF outperforms baseline methods in reliability measures.

Reliable predictions correlate with lower error rates.

Recommendation quality improves when selecting the most reliable predictions.

Abstract

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.