Recommending related articles in wikipedia via a topic-based model

Abstract

Wikipedia is currently the largest encyclopedia publicly available on the Web. In addition to keyword search and subject browsing, users may quickly access articles by following hyperlinks embedded within each article. The main drawback of this method is that some links to related articles could be missing from the current article. Also, a related article could not be inserted as a hyperlink if there is no term describing it within the current article. In this paper, we propose an approach for recommending related articles based on the Latent Dirichlet Allocation (LDA) algorithm. By applying the LDA on the anchor texts from each article, a set of diverse topics could be generated. An article can be represented as a probability distribution over this topic set. Two articles with similar topic distributions are considered conceptually related. We performed an experiment on the Wikipedia Selection for Schools which is a collection of 4,625 selected articles from the Wikipedia. Based on some initial evaluation, our proposed method could generate a set of recommended articles which are more relevant than the linked articles given on the test articles.