Relational Topic Factorization for Link Prediction in Document Networks

被引：0
|
作者：
Zhang, Wei [1 ,2 ]
Li, Jiankou [1 ,2 ]
Yong, Xi [1 ,2 ]
机构：
[1] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Informat Sci & Engn, Beijing, Peoples R China
来源：
ALGORITHMS AND MODELS FOR THE WEB GRAPH (WAW 2014) | 2014年 / 8882卷
关键词：
Link prediction; Matrix factorization; Latent Dirichlet allocation;
D O I：
10.1007/978-3-319-13123-8_8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Link prediction is one of the fundamental problems in complex networks. In this paper, we focus on link prediction in document networks, in which nodes are text documents. We propose the relational topic factorization model (RTF), a model that combines topic models and matrix factorization. We also develop an efficient Monte Carlo EM algorithm for learning the parameters. Empirical results show that our model outperforms other state-of-the-art ones, and can give better understanding of the documents.
引用
收藏
页码：96 / 107
页数：12
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