Sparse online collaborative filtering with dynamic regularization
Sparse online collaborative filtering with dynamic regularization
复制标题
DOI：
10.1016/j.ins.2019.07.093
复制
发表时间：
2019-12-01
影响因子：
8.1
通讯作者：
Alterovitz, Gil
中科院分区：
文献类型：
--
作者：
Li, Kangkang;Zhou, Xiuze;Alterovitz, Gil
协同过滤方法在推荐系统中得到了广泛的应用。传统的CF方法训练模型的成本很高，并且无法捕捉用户兴趣和项目流行度的变化。大多数CF方法假设用户兴趣在整个过程中保持不变。然而，用户的偏好总是在变化，项目的受欢迎程度总是在变化。此外，在稀疏矩阵中，已知评级数据的量非常小。提出了一种动态正则化在线协同过滤方法（OCF-DR），该方法考虑了在线协同过滤中的动态信息，并利用邻域因子来跟踪在线协同过滤中的动态变化。在MovieLens 100 K、MovieLens 1 M和HetRec 2011数据集上的实验结果表明，所提出的方法比几种基线方法有显著的改进。(C)2019爱思唯尔公司All rights reserved.
Collaborative filtering (CF) approaches are widely applied in recommender systems. Traditional CF approaches have high costs to train the models and cannot capture changes in user interests and item popularity. Most CF approaches assume that user interests remain unchanged throughout the whole process. However, user preferences are always evolving and the popularity of items is always changing. Additionally, in a sparse matrix, the amount of known rating data is very small. In this paper, we propose a method of online collaborative filtering with dynamic regularization (OCF-DR), that considers dynamic information and uses the neighborhood factor to track the dynamic change in online collaborative filtering (OCF). The results from experiments on the MovieLens100K, MovieLens1M, and HetRec2011 datasets show that the proposed methods are significant improvements over several baseline approaches. (C) 2019 Elsevier Inc. All rights reserved.