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2015 | OriginalPaper | Chapter

What Should I Read Next? A Personalized Visual Publication Recommender System

Authors : Simon Bruns, André Calero Valdez, Christoph Greven, Martina Ziefle, Ulrik Schroeder

Published in: Human Interface and the Management of Information. Information and Knowledge in Context

Publisher: Springer International Publishing

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Abstract

Discovering relevant publications for researchers is a non-trivial task. Recommender systems can reduce the effort required to find relevant publications. We suggest using a visualization- and user-centered interaction model to achieve both a more trusted recommender system and a system to understand a whole research field. In a graph-based visualization papers are aligned with their keywords according to the relevance of the keywords. Relevance is determined using text-mining approaches. By letting the user control relevance thresholds for individual keywords we have designed a recommender system that scores high in accuracy (\(\bar{x}=5.03/6\)), trust (\(\bar{x}=4.31/6\)) and usability (SUS \(\bar{x}=4.89/6\)) in a user study, while at the same time providing additional information about the field as a whole. As a result, the inherent trust issues conventional recommendation systems have seem to be less significant when using our solution.

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Footnotes
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Metadata
Title
What Should I Read Next? A Personalized Visual Publication Recommender System
Authors
Simon Bruns
André Calero Valdez
Christoph Greven
Martina Ziefle
Ulrik Schroeder
Copyright Year
2015
Book
Human Interface and the Management of Information. Information and Knowledge in Context

Print ISBN: 978-3-319-20617-2

Electronic ISBN: 978-3-319-20618-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20618-9_9