论文标题
通过Bernoulli矩阵分解为推荐系统提供可靠性
Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
论文作者
论文摘要
除了准确性之外，质量措施在现代推荐系统中也变得重要，可靠性是协作过滤中最重要的指标之一。本文提出了Bernoulli矩阵分解（BEMF），它是矩阵分解模型，以提供预测值和可靠性值。 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 模型。实验结果表明，预测的可靠性越多，错误的责任越小：选择最可靠的预测后，建议质量提高。已测试了可靠性的最先进质量指标，这表明BEMF的表现优于先前的基线方法和模型。
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.
