OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

A unified approach for learning expertise and authority in digital libraries

La Robertie, Baptiste de and Ermakova, Liana and Pitarch, Yoann and Takasu, Atsuhiro and Teste, Olivier A unified approach for learning expertise and authority in digital libraries. (2017) In: 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017), 27 March 2017 - 30 March 2017 (Suzhou, China).

[img]
Preview
(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
326kB

Official URL: https://doi.org/10.1007/978-3-319-55699-4_22

Abstract

Managing individual expertise is a major concern within any industrial-wide organization. If previous works have extensively studied the related expertise and authority profiling issues, they assume a semantic independence of these two key concepts. In digital libraries, state-of-the-art models generally summarize the researchers' profile by using solely textual information. Consequently, authors with a large amount of publications are mechanically fostered to the detriment of less prolific ones with probably higher expertise. To overcome this drawback we propose to merge the two representations of expertise and authority and balance the results by capturing a mutual reinforcement principle between these two notions. Based on a graph representation of the library, the expert profiling task is formulated as an optimization problem where latent expertise and authority representations are learned simultaneously, unbiasing the expertise scores of individuals with a large amount of publications. The proposal is instanciated on a public scientific bibliographic dataset where researchers' publications are considered as a source of evidence of individuals' expertise and citation relations as a source of authoritative signals. Results from our experiments conducted over the Microsoft Academic Search database demonstrate significant efficiency improvement in comparison with state-of-the-art models for the expert retrieval task.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. This papers appears in Part II of Volume 10178, Lecture Notes in Computer Science ISSN : 0302-9743 ISBN: 978-3-319-55699-4 The original PDF is available at: https://link.springer.com/chapter/10.1007/978-3-319-55699-4_22
HAL Id:hal-01740014
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > National Institute of Informatics - NII (JAPAN)
Other partners > Université de Lorraine (FRANCE)
Other partners > Université Paris-Est Marne-La-Vallée - UPEM (FRANCE)
Laboratory name:
Statistics:download
Deposited By: IRIT IRIT
Deposited On:12 Mar 2018 10:28

Repository Staff Only: item control page