论文标题
受限的伯努利矩阵分解：在基于分类的协作过滤中的预测准确性和覆盖范围之间平衡权衡取舍
Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
论文作者
论文摘要
与机器学习模型预测相关的可靠性措施对于增强用户对人工智能的信心至关重要。因此，那些不仅能够提供预测，而且还提供可靠性的模型享有更大的知名度。在推荐系统领域，可靠性至关重要，因为用户倾向于倾向于那些一定会感兴趣的建议，即具有高可靠性的高预测。在本文中，我们提出了限制的Bernoulli矩阵分解（RESBEMF），这是一种旨在增强基于分类的协作过滤性能的新算法。就预测质量（平均绝对误差和准确得分），预测数量（覆盖率评分）和建议质量（平均平均精度得分）而言，已将所提出的模型与文献中的其他现有解决方案进行了比较。实验结果表明，与其他建议模型相比，提出的模型在使用的质量度量方面提供了良好的平衡。
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.
