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A Recommendation System of PubMed Articles Based on Content-Based Filtering

MEDLINE/PubMed [1] is one of the largest public database on biological and medical sciences and updated daily with thousands of new papers. Biologists devote a considerable amount of time to checking PubMed to find papers relevant to their interests. To reduce their unnecessary time, several services are currently available, and typical ones are AMEDEO [2] and PubCrawler [3]. AMEDEO is operated by curators who select papers by using the topic (or the medical area, e.g. Heart failure) chosen by a user from a pre-defined topic list. On the other hand, PubCrawler is a free web service which finds relevant papers by using a keyword combination pre-fixed by a user. In both cases, the resultant report to a user is sent by an email weekly (AMEDEO) or daily (PubCrawler). Although these services are widely used, a more flexible system must be better to fulfil users’ various requests. In light of these points, we develop a so-called content-based recommendation system, which automatically captures the preference of a user. Once the preferences are acquired, the system then reports papers with high ratings to each user by email. This system provides more flexible services than those including AMEDEO and PubCrawler, and can be an option of a user with various needs.