.png){kind=icon}
返回
Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model
.png){kind=icon}
PRE
AI
DOI：10.1145/3264745.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this article, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.
Keyword:
Semi-supervised learning
latent factor model
chain graph model
data sparsity
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:
4.8
论文数:
1.3K
被引数:
4.4K
机构
引用论文
THE IMPACT OF SIZE-DEPENDENT PREDATION ON POPULATION DYNAMICS AND INDIVIDUAL LIFE HISTORY.png)
Ecology
EcologyIF0
PREAI
分享收藏The Relative Influence of Lesion Length and Other Stenosis Morphologies on Procedural Success of Coronary Intervention病变长度和其他狭窄形态对冠状动脉介入手术成功的相对影响Angiology
AngiologyIF0
PREAI
分享收藏分享收藏分享收藏
查看更多内容