PURE: a PubMed article recommendation system based on content-based filtering.
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.
Bioinformatics Center, Kyoto University, Gokasho Uji, 611-0011, Japan. t-yoneya@kirin.co.jp
Takashi
T
Yoneya
Hiroshi
H
Mamitsuka
D000465:Algorithms; D016247:Information Storage and Retrieval; D008962:Models, Theoretical; D009369:Neoplasms / Q000175:diagnosis; D011336:Probability; D039781:PubMed