Cross-Lingual News Group Recommendation Using Cluster-Based Cross-Training

Abstract

Many Web news portals have provided clustered news categories for readers to browse many related news articles. However, to the best of our knowledge, they only provide monolingual services. For readers who want to find related news articles in different languages, the search process is very cumbersome. In this paper, we propose a cross-lingual news group recommendation framework using the cross-training technique to help readers find related cross-lingual news groups. The framework is studied with different implementations of SVM and Maximum Entropy models. We have conducted several experiments with news articles from Google News as the experimental data sets. From the experimental results, we find that the proposed cross-training framework can achieve accuracy improvement in most cases.