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Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation

Knoth, Petr and Khadka, Anita (2017). Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation. In: Proceedings of the 2nd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017) (Mayr, Philipp; Chandrasekaran, Muthu Kumar and Jaidka, Kokil eds.), pp. 14–25.

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Abstract

In this paper, we build on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems. As part of this approach, we introduce three new proximity functions, extending the basic assumption of co-citation analysis (stating that the more often two articles are co-cited in a document, the more likely they are related) to take the distance between the co-cited documents into account. Ask- ing the question of whether CPA can outperform co-citation analysis in recommender systems, we have built a CPA based recommender system from a corpus of 368,385 full-texts articles and conducted a user survey to perform an initial evaluation. Two of our three proximity functions used within CPA outperform co-citations on our evaluation dataset.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 The Authors
ISSN: 1613-0073
Extra Information: co-located with the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan
Keywords: Citation Proximity Analysis; co-citation analysis; recommender system; information retrieval
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 51763
Depositing User: Kay Dave
Date Deposited: 30 Oct 2017 10:02
Last Modified: 30 Oct 2017 10:05
URI: http://oro.open.ac.uk/id/eprint/51763
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