使用梯度增强的一般功能矩阵分解

使用梯度增强的一般功能矩阵分解(中文5000字,英文3000字)
摘要
矩阵分解是最成功的协同过滤技术之一。协同过滤的一个挑战是如何利用可用的辅助信息来提高预测准确性。在本文中，我们研究了利用辅助信息作为因子分解特征的问题，并提出将问题形式化为一般功能矩阵分解，其模型包括传统的矩阵分解模型作为其特殊情况。此外，我们提出了一种基于梯度增强的算法来有效地解决优化问题。最后，我们给出了两个特定算法，用于两个特定任务的高效特征函数构造。我们的方法可以通过在训练数据的基础上搜索无限功能空间来构造更合适的特征函数，从而可以产生更好的预测精度。实验结果表明，该方法优于三个真实世界数据集的基线方法。
General Functional Matrix Factorization Using Gradient Boosting
Abstract
Matrix factorization is among the most successful techniques for collaborative filtering. One challenge of collaborative filtering is how to utilize available auxiliary information to improve prediction accuracy. In this paper, we study the problem of utilizing auxiliary information as features of factorization and propose formalizing the problem as general functional matrix factorization, whose model includes conventional matrix factorization models as its special cases. Moreover, we propose a gradient boosting based algorithm to efficiently solve the optimization problem. Finally, we give two specific algorithms for efficient feature function construction for two specific tasks. Our method can construct more suitable feature functions by searching in an infinite functional space based on training data and thus can yield better prediction accuracy. The experimental results demonstrate that the proposed method outperforms the baseline methods on three realworld datasets.