.png){kind=icon}
返回
Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.10.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recommender systems have become an important research area because of a high interest from academia and industries. As a branch of recommender systems, collaborative filtering (CF) systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations. However, CF often confronts the sparsity problem, caused by fewer ratings against the unknowns that need to be predicted. In this paper, we consider a hybrid approach that combines content-based approach with collaborative filtering under a unified model called co-clustering with augmented matrices (CCAM). CCAM is based on information-theoretic co-clustering but further considers augmented data matrices like user profile and item description. By presenting results with a reduced error of prediction, we show that content-based information can help reduce the sparsity problem through minimizing the mutual information loss of the three data matrices based on CCAM. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Co-clustering
Collaborative filtering
Mutual information
Recommender system
Augmented data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Collaborative filtering using orthogonal nonnegative matrix tri-factorization基于正交非负矩阵三分解的协同过滤算法
.png){kind=icon}
PREAI
分享收藏
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容