论文标题
理论上准确的基于基质分解的推荐系统的正则化技术
Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems
论文作者
论文摘要
正则化是解决机器学习算法过度拟合问题的流行技术。大多数正则化技术依赖于正则化系数的参数选择。插入方法和交叉验证方法是回归方法的两种最常见的参数选择方法，例如脊回归，套索回归和内核回归。基于矩阵分解的推荐系统也非常依赖正则化技术。大多数人选择单个标量值来独立或集体地正规化用户功能向量和项目特征向量。在本文中，我们证明选择正则化系数的这种方法是无效的，并且我们提供了一种理论上准确的方法，该方法在准确性和公平度量方面都超过了最广泛使用的方法。
Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression and Kernel Regression. Matrix factorization based recommendation system also has heavy reliance on the regularization technique. Most people select a single scalar value to regularize the user feature vector and item feature vector independently or collectively. In this paper, we prove that such approach of selecting regularization coefficient is invalid, and we provide a theoretically accurate method that outperforms the most widely used approach in both accuracy and fairness metrics.
