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

Raamkumar, Aravind Sesagiri; Foo, Schubert; Pang, Natalie

doi:10.1016/j.ipm.2016.12.006

Cited by 45 publications

(13 citation statements)

References 46 publications

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“…Without RLs, an information seeker is exposed to various factors affecting the information retrieval process, that is, semantics, ad hoc searches, experience of using a search engine and search engine efficiency [1][2][3]. RLs are also created for purposes of research and various academic activities [4][5][6]. Purposefully, this research will focus on pedagogical RLs.…”

Section: Background and Proposed Research 1reading Listsmentioning

confidence: 99%

“…However, there is a much larger challenge that needs to be addressed, which is localised intelligence. As an RL is created by the recommendation of RIs by an academic belonging to a specific HEI, the recommendations are restricted to an academic’s knowledge of their field, semantics, experience and awareness of developments and so on [4]. Hence, the students may be missing the opportunity to read the best RIs for a particular course.…”

Section: Background and Proposed Researchmentioning

confidence: 99%

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A recommendation-based reading list system prototype for learning and resource management

Chowdhury

Koya

Bugaje

2021

Journal of Information Science

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A reading list is a list of reading items recommended by an academic to assist students’ acquisition of knowledge for a specific subject. Subsequently, the libraries of higher education institutions collect and assemble reading lists according to specific courses and offer the students the reading list service. However, the reading list is created based on localised intelligence, restricted to the academic’s knowledge of their field, semantics, experience and awareness of developments. This investigation aims to present the views and comments of academics, and library staff, on an envisaged aggregated reading list service, which aggregates recommended reading items from various higher education institutions. This being the aim, we build a prototype, which aggregates reading lists from different universities and showcase it to 19 academics and library staff in various higher education institutions to capture their views, comments and any recommendations. In the process, we also showcase the feasibility of collecting and aggregating reading lists, in addition to understanding the process of reading lists creation at their respective higher education institutions. The prototype successfully showcases the creation of ranked lists of reading items, authors, topics, modules and courses. Academics and library staff indicated that aggregated lists would collectively benefit the academic community. Consequently, recommendations in the form of process implementations and technological applications are made to overcome and successfully implement the proposed aggregated reading list service. This proof-of-concept demonstrates potential benefits for the academic community and identifies further challenges to overcome in order to scale it up to the implementation stage.

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Section: Background and Proposed Research 1reading Listsmentioning

confidence: 99%

Section: Background and Proposed Researchmentioning

confidence: 99%

A recommendation-based reading list system prototype for learning and resource management

Chowdhury

Koya

Bugaje

2021

Journal of Information Science

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“…Since four types of papers constitute the RL, a different processing component was required for building the RL. We proposed a paper ranking technique (Sesagiri Raamkumar et al, 2017b) based on a novel coverage value known as the topical and peripheral coverage (TPC) (Raamkumar et al, 2015b). This value is in turn based on author-specified keywords and the citations network.…”

Section: Proposed Interventionsmentioning

confidence: 99%

Evaluating a threefold intervention framework for assisting researchers in literature review and manuscript preparatory tasks

Raamkumar

Foo

Pang

2017

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Purpose Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system. Design/methodology/approach A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data. Findings Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group. Originality/value This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.

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“…There is a demand for improvement in methods for patient-specific paper retrieval, as evidenced by the TREC precision medicine track (Roberts et al, 2017). State-of-the-art methods for paper retrieval rely on term lists generated by experts or users, or automated information retrieval of similar papers (Sesagiri Raamkumar, Foo, & Pang, 2017;Wesley-Smith & West, 2016). These methods have several disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Context-aware query design combines knowledge and data for efficient reading and reasoning

Holtzapple¹,

Cochran²,

Miskov-Zivanov³

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.

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

Cited by 45 publications

References 46 publications

A recommendation-based reading list system prototype for learning and resource management

A recommendation-based reading list system prototype for learning and resource management

Evaluating a threefold intervention framework for assisting researchers in literature review and manuscript preparatory tasks

Context-aware query design combines knowledge and data for efficient reading and reasoning

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