The Journal of Machine Learning Research
Using latent topics to enhance search and recommendation in Enterprise Social Software
Expert Systems with Applications: An International Journal
Finding relevant missing references in learning courses
Proceedings of the 22nd international conference on World Wide Web companion
Personalized recommendation based on review topics
Service Oriented Computing and Applications
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The 2007 Wikipedia Selection for Schools is a collection of 4,625 selected articles from Wikipedia as educational for children. Users can currently access articles within the collection via two different methods: (1) by browsing on either a subject index or a title index sorted alphabetically, and (2) by following hyperlinks embedded within article pages. These two retrieval methods are considered static and subjected to human editors. In this paper, we apply the Latent Dirichlet Allocation (LDA) algorithm to generate a topic model from articles in the collection. Each article can be expressed by a probability distribution on the topic model. We can recommend related articles by calculating the similarity measures among the articles' topic distribution profiles. Our initial experimental results showed that the proposed approach could generate many highly relevant articles, some of which are not covered by the hyperlinks in a given article.