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Towards effective research recommender systems for repositories

Knoth, Petr; Anastasiou, Lucas; Charalampous, Aristotelis; Cancellieri, Matteo; Pearce, Samuel; Pontika, Nancy and Bayer, Vaclav (2017). Towards effective research recommender systems for repositories. In: Open Repositories 2017, 26 -30 June 2017, Brisbane, Australia.

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Abstract

In this paper, we argue why and how the integration of recommender systems for research can enhance the functionality and user experience in repositories. We present the latest technical innovations in the CORE Recommender, which provides research article recommendations across the global network of repositories and journals. The CORE Recommender has been recently redeveloped and released into production in the CORE system and has also been deployed in several third-party repositories. We explain the design choices of this unique system and the evaluation processes we have in place to continue raising the quality of the provided recommendations. By drawing on our experience, we discuss the main challenges in offering a state-of-the-art recommender solution for repositories. We highlight two of the key limitations of the current repository infrastructure with respect to developing research recommender systems: 1) the lack of a standardised protocol and capabilities for exposing anonymised user-interaction logs, which represent critically important input data for recommender systems based on collaborative filtering and 2) the lack of a voluntary global sign-on capability in repositories, which would enable the creation of personalised recommendation and notification solutions based on past user interactions.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 The Author(s)
Keywords: repositories; recommender; recommendation systems
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: 49366
Depositing User: Nancy Pontika
Date Deposited: 09 May 2017 14:38
Last Modified: 10 Jul 2017 10:03
URI: http://oro.open.ac.uk/id/eprint/49366
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