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Overview of the CLEF eHealth Evaluation Lab 2021
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In: Experimental IR Meets Multilinguality, Multimodality, and Interaction ; https://hal.archives-ouvertes.fr/hal-03369846 ; Experimental IR Meets Multilinguality, Multimodality, and Interaction, 12880, Springer International Publishing, pp.308-323, 2021, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-85251-1_21⟩ (2021)
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Overview of the CLEF 2019 Personalised Information Retrieval Lab (PIR-CLEF 2019)
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In: CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction ; https://hal.archives-ouvertes.fr/hal-03156689 ; CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction, pp.417-424, 2019, ⟨10.1007/978-3-030-28577-7_31⟩ (2019)
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Overview of the CLEF eHealth Evaluation Lab 2019
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In: CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp 322-339 ; https://hal.archives-ouvertes.fr/hal-03156710 ; CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp 322-339, pp.322-339, 2019, ⟨10.1007/978-3-030-28577-7_26⟩ (2019)
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Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes
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The Scholarly Influence of the CLEF eHealth Initiative by the Conference and Labs of the Evaluation Forum: Review and Bibliometric Study of the 2012-2017 Outcomes.
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Experimental IR meets multilinguality, multimodality, and interaction: 8th international conference of the CLEF association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, proceedings
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CLEF 2017 NewsREEL Overview: A Stream-based Recommender Task for Evaluation and Education
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Abstract:
News recommender systems provide users with access to news stories that they find interesting and relevant. As other online, stream-based recommender systems, they face particular challenges, including limited information on users’ preferences and also rapidly fluctuating item collections. In addition, technical aspects, such as response time and scalability, must be considered. Both algorithmic and technical considerations shape working requirements for real-world recommender systems in businesses. NewsREEL represents a unique opportunity to evaluate recommendation algorithms and for students to experience realistic conditions and to enlarge their skill sets. The NewsREEL Challenge requires participants to conduct data-driven experiments in NewsREEL Replay as well as deploy their best models into NewsREEL Live’s ‘living lab’. This paper presents NewsREEL 2017 and also provides insights into the effectiveness of NewsREEL to support the goals of instructors teaching recommender systems to students. We discuss the experiences of NewsREEL participants as well as those of instructors teaching recommender systems to students, and in this way, we showcase NewsREEL’s ability to support the education of future data scientists.
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URL: http://eprints.gla.ac.uk/142655/13/142655.pdf
http://eprints.gla.ac.uk/142655/
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