Clustering and recommendation of scientific documentation based on the topic model

Abstract

In this paper, we propose a novel and efficient method for topic modeling and clustering of scientific documentation. It is a technology of content-based filtering and aims to find the same topics. Incorporating topic features will enhance the accuracy of document clustering methods. Based on the clustering results, we use the method of calculating similarity of scientific documentation to get the related documentation consistent with the content. The ranking of recommendation is according to the value of similarity of documentation. Clustering results are evaluated by F-measure. Empirical study on real-world datasets shows that the LDA's performance is better than PLSA's in the document clustering. Meantime, we find the proper number of topics in document representation. © 2013 Springer-Verlag.