Joeran Beel
Stefan Langer
Andreas Nürnberger
ABSTRACT
In this demo paper we present Docear's research paper recommender system. Docear is an academic literature suite to search, organize, and create research articles. The users' data (papers, references, annotations, etc.) is managed in mind maps and these mind maps are utilized for the recommendations. Using content-based filtering methods, Docear's recommender achieves click-through rates around 6%, in some scenarios even over 10%.
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Cross Ref
- Beel, J., Gipp, B., Langer, S. and Genzmehr, M. 2011. Docear: An Academic Literature Suite for Searching, Organizing and Creating Academic Literature. Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries (2011), 465--466. Google ScholarDigital Library
- Bogers, T. and Bosch, A. van den 2008. Recommending scientific articles using citeulike. Proceedings of the 2008 ACM conference on Recommender systems (2008), 287--290. Google ScholarDigital Library
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- Pohl, S., Radlinski, F. and Joachims, T. 2007. Recommending Related Papers Based on Digital Library Access Records. Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries (Vancouver, BC, Canada, 2007), 417--418. Google ScholarDigital Library
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Index Terms
- Introducing Docear's research paper recommender system
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