Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80799
Title: Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems
Authors: Sesagiri Raamkumar, Aravind
Foo, Schubert
Pang, Natalie
Keywords: Reading list
Literature review
Issue Date: 2017
Source: Sesagiri Raamkumar, A., Foo, S., & Pang, N. (2017). Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems. Information Processing and Management, 53(3), 577-594.
Series/Report no.: Information Processing and Management
Abstract: An initial reading list is prepared by researchers at the start of literature review for getting an overview of the research performed in a particular area. Prior studies have taken the approach of merely recommending seminal or popular papers to aid researchers in such a task. In this paper, we present an alternative technique called the AKR (Author-specified Keywords based Retrieval) technique for providing popular, recent, survey and a diverse set of papers as a part of the initial reading list. The AKR technique is based on a novel coverage value that has its calculation centered on author-specified keywords. We performed an offline evaluation experiment with four variants of the AKR technique along with three state-of-the-art approaches involving collaborative filtering and graph ranking algorithms. Findings show that the Hyperlink-Induced Topic Search (HITS) enhanced variant of the AKR technique performs better than other techniques, satisfying most requirements for a reading list. A user evaluation study was conducted with 132 researchers to gauge user interest on the proposed technique using 14 evaluation measures. Results show that (i) students group are more satisfied with the recommended papers than staff group, (ii) popularity measure is strongly correlated with the output quality measures and (iii) the measures familiarity, usefulness and ‘agreeability on a good list’ were found to be strong predictors for user satisfaction. The AKR technique provides scope for extension in future information retrieval and content-based recommender systems studies.
URI: https://hdl.handle.net/10356/80799
http://hdl.handle.net/10220/42245
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2016.12.006
Rights: © 2016 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Information Processing and Management, Elsevier Ltd. 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/10.1016/j.ipm.2016.12.006].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:WKWSCI Journal Articles

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