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NLP Reading Group: Collaborative topic modeling for recommending scientific articles

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Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering.

@inproceedings{wang2011collaborative, title={Collaborative topic modeling for recommending scientific articles}, author={Wang, C. and Blei, D.M.}, booktitle={Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining}, pages={448—456}, year={2011}, organization={ACM} }

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