半监督偏好学习算法  

Semi-supervised Preference Learning Algorithm

作　　者：赵敏 刘惊雷 ZHAO Min;LIU Jinglei(School of Computer and Control Engineering,Yantai University,Yantai 264005)

机构地区：[1]烟台大学计算机与控制工程学院

出　　处：《模式识别与人工智能》2019年第10期909-916,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61572419,61773331,61703360)资助~~

摘　　要：为了解决推荐系统的鲁棒性和重构精度问题,文中提出半监督偏好学习算法,通过偏好学习获得潜在偏好,实现推荐.使用l 2,1范数作为优化目标函数的正则项,消除噪声和异常点.采用图的拉普拉斯调节整合用户-项目矩阵的单边信息,实现多图融合,提高推荐精度.在Movielens 10M数据集和Netflix数据集上的实验验证文中算法精度较高、速度较快、鲁棒性较高.To improve low robustness and reconstruction accuracy of recommendation system,a semi-supervised preference learning algorithm is proposed to obtain potential preferences via preference learning and implement recommendations.The l 2,1 norm is utilized as the regularization of the optimization objective function to eliminate the noises and outliers.The graph Laplacian regularization is employed to integrate the side information of UI matrix to realize multi-image fusion and improve recommendation precision.The experiments on Movielens 10M and Netflix datasets indicate that the proposed algorithm produces high precision,speed and robustness.

关 键 词：半监督 偏好学习 鲁棒性 图拉普拉斯调节 l2 1范数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]