Article recommendation based on a topic model for Wikipedia Selection for Schools

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Article recommendation based on a topic model for Wikipedia Selection for Schools is a 2008 conference paper written in English by Haruechaiyasak C., Damrongrat C. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

[edit] Abstract

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.

[edit] References

This publication has 0 references. Only those references related to wikis are included here: Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993-1022 (2003)Steyvers, M., Griffiths, T., Probabilistic topic models (2006) Latent Semantic Analysis: A Road to Meaning, , Landauer, T, McNamara, D, Dennis. S, Kintsch, W, eds, Lawrence Erlbaum, Mahwah

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