Scholarly paper recommendation via user's recent research interests

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Scholarly paper recommendation via user's recent research interests
We examine the effect of modeling a researcher’s past works in recommending scholarly papers to the researcher. Our hypothesis is that an author’s published works constitute a clean signal of the latent interests of a researcher. A key part of our model is to enhance the profile derived directly from past works with information coming from the past works’ referenced papers as well as papers that cite the work. In our experiments, we differentiate between junior researchers that have only published one paper and senior researchers that have multiple publications. We show that filtering these sources of information is advantageous – when we additionally prune noisy citations, referenced papers and publication history, we achieve statistically significant higher levels of recommendation accuracy. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information filtering, Search process; H.3.7 [Digital Libraries]: Systems issues, User issues General Ter...
Kazunari Sugiyama, Min-Yen Kan
Added 10 Jul 2010
Updated 10 Jul 2010
Type Conference
Year 2010
Where JCDL
Authors Kazunari Sugiyama, Min-Yen Kan
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