Semi-supervised learning by constructing query-document heterogeneous information network

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.

Keywords

• heterogeneous information networks;semi-supervised learning;information retrieval;click-through data