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C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

Färber, Michael ORCID iD icon; Zinecker, Vinzenz; Cartus, Isabela Bragaglia; Celis, Sebastian; Duma, Maria

Abstract:

Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users’ satisfaction.


Verlagsausgabe §
DOI: 10.5445/IR/1000134685
Veröffentlicht am 04.07.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 04.2021
Sprache Englisch
Identifikator ISBN: 978-1-4503-8313-4
KITopen-ID: 1000134685
Erschienen in Companion Proceedings of the Web Conference 2021
Verlag Association for Computing Machinery (ACM)
Seiten 441–445
Vorab online veröffentlicht am 19.04.2021
Nachgewiesen in Scopus
Dimensions
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