RefMed: A New System for Finding Relevant Articles on PubMed
Author Information
Author(s): Yu Hwanjo, Kim Taehoon, Oh Jinoh, Ko Ilhwan, Kim Sungchul, Han Wook-Shin
Primary Institution: POSTECH, Pohang, South Korea
Hypothesis
Can a multi-level relevance feedback system improve the accuracy of article retrieval on PubMed?
Conclusion
RefMed is the first multi-level relevance feedback system for PubMed, achieving high accuracy with less user feedback.
Supporting Evidence
- RefMed integrates RankSVM into a DBMS to improve processing time.
- The system allows users to provide multi-level feedback, enhancing relevance learning.
- RefMed achieves high accuracy with less user feedback compared to traditional methods.
Takeaway
RefMed helps people find the right articles on PubMed by letting them give feedback on what they like, making it smarter over time.
Methodology
RefMed uses RankSVM for learning relevance from user feedback and integrates it into a relational database management system for real-time processing.
Limitations
The system's performance may depend on the amount and quality of user feedback provided.
Digital Object Identifier (DOI)
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