Abstract Matrix Factorization is a widely used technique for modeling pairwise and matrix-like data. It is frequently used in pattern recognition, topic analysis and other areas. Side information is often available, however utilization of this additional information is problematic in the pure matrix factorization framework. This article proposes a novel method of utilizing side information by combining arbitrary nonlinear Quantile Regression model and Matrix Factorization under Bayesian framework. Gradient-free optimization procedure with the novel Surrogate Function is used to solve the resulting MAP estimator. The model performance has been evaluated on real data-sets.

中文翻译：

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将边信息合并到具有贝叶斯分位数回归的稳健矩阵分解中

摘要 矩阵分解是一种广泛使用的技术，用于对成对和类矩阵数据进行建模。它经常用于模式识别、主题分析等领域。辅助信息通常是可用的，但是在纯矩阵分解框架中使用这些附加信息是有问题的。本文提出了一种在贝叶斯框架下结合任意非线性分位数回归模型和矩阵分解来利用边信息的新方法。使用具有新颖代理函数的无梯度优化程序来求解生成的 MAP 估计量。模型性能已经在真实数据集上进行了评估。