Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems

• Main Authors: Sesagiri Raamkumar, Aravind, Foo, Schubert, Pang, Natalie
• Other Authors: Wee Kim Wee School of Communication and Information
• Format: Article
• Language: English
• Published: 2017
• Subjects: Reading list; Literature review
• Online Access: https://hdl.handle.net/10356/80799; http://hdl.handle.net/10220/42245
• Institution: Nanyang Technological University

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.