Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86320
Title: Multi-method evaluation in scientific paper recommender systems
Authors: Sesagiri Raamkumar, Aravind
Foo, Schubert
Keywords: Scientific Paper Recommender Systems
Multi-method Evaluation
Issue Date: 2018
Source: Sesagiri Raamkumar, A., & Foo, S. (2018). Multi-method evaluation in scientific paper recommender systems. UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, 179-182.
Abstract: Recommendation techniques in scientific paper recommender systems (SPRS) have been generally evaluated in an offline setting, without much user involvement. Nonetheless, user relevance of recommended papers is equally important as system relevance. In this paper, we present a scientific paper recommender system (SPRS) prototype which was subject to both offline and user evaluations. The lessons learnt from the evaluation studies are described. In addition, the challenges and open questions for multi-method evaluation in SPRS are presented.
URI: https://hdl.handle.net/10356/86320
http://hdl.handle.net/10220/45136
DOI: http://dx.doi.org/10.1145/3213586.3226215
Rights: © 2018 ACM. This is the author created version of a work that has been peer reviewed and accepted for publication by UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, ACM. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/3213586.3226215].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:WKWSCI Conference Papers

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