An exploratory study on content-based filtering of call for papers

Martin, German Hurtado, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881, Cornelis, Chris and Naessens, Helga 2013. An exploratory study on content-based filtering of call for papers. Presented at: IRFC 2013: 6th Information Retrieval Facility Conference, Limassol, Cyprus, 7-9 October 2013. Published in: Lupu, M., Kanoulas, E. and Loizides, F. eds. Multidisciplinary Information Retrieval: 6th Information Retrieval Facility Conference, IRFC 2013, Limassol, Cyprus, October 7-9, 2013. Proceedings. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.8201 Berlin and Heidelberg: Springer, pp. 58-69. 10.1007/978-3-642-41057-4_7

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Abstract

Due to the increasing number of conferences, researchers need to spend more and more time browsing through the respective calls for papers (CFPs) to identify those conferences which might be of interest to them. In this paper we study several content-based techniques to filter CFPs retrieved from the web. To this end, we explore how to exploit the information available in a typical CFP: a short introductory text, topics in the scope of the conference, and the names of the people in the program committee. While the introductory text and the topics can be directly used to model the document (e.g. to derive a tf-idf weighted vector), the names of the members of the program committee can be used in several indirect ways. One strategy we pursue in particular is to take into account the papers that these people have recently written. Along similar lines, to find out the research interests of the users, and thus to decide which CFPs to select, we look at the abstracts of the papers that they have recently written. We compare and contrast a number of approaches based on the vector space model and on generative language models.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISBN:	9783642410574
ISSN:	0302-9743
Last Modified:	25 Oct 2022 09:42
URI:	https://orca.cardiff.ac.uk/id/eprint/59682

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