Multi-method evaluation in scientific paper recommender systems

Multi-method evaluation in scientific paper recommender systems

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 recomm...

Full description

Saved in:
Bibliographic Details
Main Authors: Sesagiri Raamkumar, Aravind, Foo, Schubert
Other Authors: Wee Kim Wee School of Communication and Information
Format: Conference or Workshop Item
Language:English
Published: 2018
Subjects:
Scientific Paper Recommender Systems
Multi-method Evaluation
Online Access:https://hdl.handle.net/10356/86320
http://hdl.handle.net/10220/45136
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-86320
record_format dspace
spelling sg-ntu-dr.10356-863202020-03-07T12:15:48Z Multi-method evaluation in scientific paper recommender systems Sesagiri Raamkumar, Aravind Foo, Schubert Wee Kim Wee School of Communication and Information UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization Scientific Paper Recommender Systems Multi-method Evaluation 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. NRF (Natl Research Foundation, S’pore) Accepted version 2018-07-19T06:46:03Z 2019-12-06T16:20:19Z 2018-07-19T06:46:03Z 2019-12-06T16:20:19Z 2018 Conference Paper 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. https://hdl.handle.net/10356/86320 http://hdl.handle.net/10220/45136 10.1145/3213586.3226215 en © 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]. 4 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Scientific Paper Recommender Systems
Multi-method Evaluation
spellingShingle Scientific Paper Recommender Systems
Multi-method Evaluation
Sesagiri Raamkumar, Aravind
Foo, Schubert
Multi-method evaluation in scientific paper recommender systems
description 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.
author2 Wee Kim Wee School of Communication and Information
author_facet Wee Kim Wee School of Communication and Information
Sesagiri Raamkumar, Aravind
Foo, Schubert
format Conference or Workshop Item
author Sesagiri Raamkumar, Aravind
Foo, Schubert
author_sort Sesagiri Raamkumar, Aravind
title Multi-method evaluation in scientific paper recommender systems
title_short Multi-method evaluation in scientific paper recommender systems
title_full Multi-method evaluation in scientific paper recommender systems
title_fullStr Multi-method evaluation in scientific paper recommender systems
title_full_unstemmed Multi-method evaluation in scientific paper recommender systems
title_sort multi-method evaluation in scientific paper recommender systems
publishDate 2018
url https://hdl.handle.net/10356/86320
http://hdl.handle.net/10220/45136
_version_ 1681040030441668608

Similar Items