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Recommendations in the Scientific World

In the current landscape of scientific research, with over 739,303 articles published in 2006 alone and rejection rates exceeding 83% in top journals, efficient filtering and merit evaluation have become paramount. This presentation explores the challenges faced by researchers in navigating the vast amount of literature and introduces advanced recommendation systems like Journalfire. These systems utilize content-based and collaborative filtering methods, offering tailored recommendations based on users' past preferences. By democratizing research access and leveraging hybrid approaches, we aim to enhance discovery and collaboration in science.

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Recommendations in the Scientific World

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  1. Recommendations in the Scientific World John Delacruz Oct 8, 2007

  2. Outline • Problems in science • Finding papers • Filtering and Merit • Journalfire • What is it? • Data set • Other data sets

  3. Current Problem (Biology) • Finding papers • 739,303 articles in 2006 • ~20 journals per discipline • Filtering and merit • Journal title • 83% rejection rate

  4. Solution • Recommendation system • Democratic rating system • New measures for merit • Stop relying on journal title

  5. Journalfire

  6. Recommendation systems • Content-based recommendations • The user will be recommended items similar to the ones the user preferred in the past • Collaborative recommendations • The user will be recommended items that people with similar tastes and preferences liked in the past • Hybrid approaches • These methods combine collaborative • and content-based methods Adomavicius and Tuzhilin, 2005

  7. Pubmed Title Authors Journal Abstract MESH terms Date Journalfire Rating Tags Journal clubs User Favorites Date Journalfire data

  8. Content based recommendations author author keywords article article journal date tags

  9. Collaborative recommendations Adomavicius and Tuzhilin, 2005 article user lists article co-occurrence of articles

  10. New collaborators keywords article article author author journal journal date

  11. Other data sets

  12. Faculty of 1000 6,500 ratings

  13. Neurotree

  14. Tagging

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