PURE: A PubMed Article Recommendation System Based on Content-Based Filtering

Takashi Yoneya[1][2] (t-yoneya@kirin.co.jp)
Hiroshi Mamitsuka[1] (mami@kuicr.kyoto-u.ac.jp)

[1]Bioinformatics Center, Kyoto University, Gokasho Uji, 611-0011, Japan
[2]Discovery Research Laboratories, Kirin Pharma Co. Ltd., 3 Miyahara, Takasaki, Gunma 370-1295, Japan

Abstract

We have developed a PubMed article recommendation system, PURE, which is based on content-based filtering. PURE has a web interface by which users can add/delete their preferred articles. Once articles are registered, PURE then performs model-based clustering of the preferred articles and recommends the highly-rated articles by the prediction using the trained model. PURE updates the PubMed articles and reports the recommendation by email on daily-base. This system will be helpful for biologists to reduce the time required for gathering information from PubMed. PURE is downloadable under GPL license, via www.bic.kyoto-u.ac.jp/pathway/mami/out/PURE.tar.gz.

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