基于查询—文档异构信息网络的半监督学习

刘钰峰; 李仁发

doi:10.3969/j.issn.1000-436x.2014.08.006

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基于查询—文档异构信息网络的半监督学习

学术论文 | 更新时间：2024-10-11

- 基于查询—文档异构信息网络的半监督学习
- Semi-supervised learning by constructing query-document heterogeneous information network
- 通信学报 2014年35卷第8期页码：40-47
- 作者机构：
  
  1. 湖南大学信息科学与工程学院，湖南长沙 410082
  2. 湖南大学嵌入式系统与网络实验室，湖南长沙 410082
- 作者简介：
  
  [ "刘钰峰（1974-），男，湖南邵阳人，湖南大学博士生，主要研究方向为智能信息检索与机器学习。" ]
  [ "李仁发（1956-），男，湖南郴州人，湖南大学教授、博士生导师，主要研究方向为嵌入式计算、网络计算和无线网络。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61173036)
- DOI：10.3969/j.issn.1000-436x.2014.08.006
  中图分类号： TP391
- 网络出版日期：2014-08，
  
  纸质出版日期：2014-08-25
- 稿件说明：
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刘钰峰, 李仁发. 基于查询—文档异构信息网络的半监督学习[J]. 通信学报, 2014,35(8):40-47.

Yu-feng LIU, Ren-fa LI. Semi-supervised learning by constructing query-document heterogeneous information network[J]. Journal on communications, 2014, 35(8): 40-47.
刘钰峰, 李仁发. 基于查询—文档异构信息网络的半监督学习[J]. 通信学报, 2014,35(8):40-47. DOI： 10.3969/j.issn.1000-436x.2014.08.006.

Yu-feng LIU, Ren-fa LI. Semi-supervised learning by constructing query-document heterogeneous information network[J]. Journal on communications, 2014, 35(8): 40-47. DOI： 10.3969/j.issn.1000-436x.2014.08.006.

摘要

基于图的半监督学习近年来得到了广泛的研究，然而，现有的半监督学习算法大都只能应用于同构网络。根据查询及文档自身的内容特征和点击关系构建查询—文档异构信息网络，并引入样本的判别信息强化网络结构。提出了查询—文档异构信息网络上半监督聚类的正则化框架和迭代算法，在正则化框架中，基于流形假设构造了异构信息网络上的代价函数，并得到该函数的封闭解，以此预测未标记查询和文档的类别标记。在大规模商业搜索引擎查询日志上的实验表明本方法优于传统的半监督学习方法。

Abstract

Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever

although classification on homogeneous networks has been studied for decades

classification on heterogeneous networks has not been explored until recently. The semi-supervised classification problem on query-document heteroge-neous information network which incorporate the bipartite graph with the content information from both sides is consid-ered. In order to strengthen the network structure

class information of sample nodes is introduced. A semi-supervised learning algorithm based on two frameworks including the novel graph-based regularization framework and the iterative framework is investigated. In the regularization framework

a new cost function to consider the direct relationship be-tween two entity sets and the content information from both sides which leads to a significant improvement over the baseline methods is developed. Experimental results demonstrate that proposed method achieves the best performance with consistent and promising improvements.

关键词

Keywords

references

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