Abstract
Social reference management systems, such as Mendeley, Zotero or CiteUlike offer many services to their users: finding and managing references, finding other users, grouping users with similar research interests. Harnessing these systems to build personalized recommendations could be useful both for novice researchers (graduate students) and for experienced researchers to keep them updated in their areas. We propose a trust-based hybrid recommender system that infers the user’s ratings of papers and builds a social trust network for an area of recent research interest. We will evaluate the accuracy of predicting the most relevant papers for the current interest and experience level of the researcher and the user satisfaction of the system.
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Alotaibi, S., Vassileva, J. (2013). Trust-Based Recommendations for Scientific Papers Based on the Researcher’s Current Interest. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_96
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DOI: https://doi.org/10.1007/978-3-642-39112-5_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
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