Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
复制标题
DOI：
10.1016/j.knosys.2011.09.006
复制
发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Xin Luo;Yunni Xia;Qingsheng Zhu
中科院分区：
文献类型：
--
作者：
Xin Luo;Yunni Xia;Qingsheng Zhu
由于具有高精度和可扩展性，基于矩阵分解（MF）的模型在构建协作过滤（CF）推荐器时变得很流行。然而，目前基于MF的模型大多是批量模型，无法增量更新；而在现实世界的应用程序中，用户总是喜欢在做出反馈后收到系统的快速响应。在这项工作中，我们的目标是设计一个基于正则化矩阵分解（RMF）的增量CF推荐器。为了实现这一目标，我们首先简化了RMF的训练规则，提出了SI-RMF，为进一步研究提供了一个简单的数学形式；由此我们设计了两种增量RMF模型，分别是增量RMF（IRMF）和带有线性偏差的增量RMF（IRMF-B）。在两个大型真实数据集上进行的实验表明了积极的结果，证明了我们策略的有效性。
The Matrix-Factorization (MF) based models have become popular when building Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability. However, most of the current MF based models are batch models that are incapable of being incrementally updated; while in real world applications users always enjoy receiving quick responses from the system once they have made feedbacks. In this work, we aim to design an incremental CF recommender based on the Regularized Matrix Factorization (RMF). To achieve this objective, we first simplify the training rule of RMF to propose the SI-RMF, which provides a simple mathematic form for further investigation; whereby we design two Incremental RMF models, respectively are the Incremental RMF (IRMF) and the Incremental RMF with linear biases (IRMF-B). The experiments on two large, real datasets suggest positive results, which prove the efficiency of our strategy.