Toward Altmetric-Driven Research-Paper Recommender System Framework

Maake, Benard Magara; Ojo, Sunday O.; Zuva, Tranos

doi:10.1109/sitis.2017.21

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…However, the task of selecting the most representative keywords is more difficult once the literature of a domain attains a certain size (Hu et al, 2018). This issue has been particularly problematic for academic search tasks (Hoeber and Khazaei, 2015; Magara et al , 2017) and is central to the current study. The ubiquity of this challenge is evidenced by the number of extraction and analysis methods to improve queries – some of which have been reviewed in Beliga (2014), Beliga et al (2015) and Gupta (2017).…”

Section: Rationalementioning

confidence: 99%

Digital library keyword analysis for visualization education research

Bratt

2018

JARHE

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Purpose The purpose of this paper is to assess the efficacy of the Institute of Electrical and Electronics Engineers (IEEE) Xplore digital library search engine to return relevant materials on information visualization pedagogy literature and to recommend search strategies to assist the digital library academic readership improve the efficacy of their search tasks. Furthermore, the results are of interest to general readers using similar digital repositories. Design/methodology/approach An initial scoping review using EBSCO Discovery services returned the number and accessibility of sources and publications-based various Boolean searches. A revised search strategy focused the search to IEEE publications as the primary source of visualization research. A corpus of keywords were extracted from the 44 relevant articles and analyzed for relevance, keyword trends and contexts of use. Findings Keyword analysis results show visualization education research is confounded by several information retrieval issues: relevancy, incomplete taxonomy, non-standard lexicon, diverse disciplines and under-representation. Recommendations include: search strategies, alternative digital collections, a potential opportunity for research in information visualization pedagogy to address this gap in an emerging field and the need for more effective interactive tools to assist with keyword selection. Research limitations/implications The study focused on the IEEE publications as the primary source of visualization research. Practical implications A repository of visualization education research that is easily findable and relevant benefits both faculty using information visualization in their teaching and academics whose work must be disseminated to the broadest audience. Strategic keyword selection, interactive keyword tools or more robust thesaurus will enable IEEE Xplore digital library users to optimize their interaction with the system. Furthermore, results suggest a need for more research in information visualization pedagogy. Originality/value This is the only study to uniquely assess the efficacy of the IEEE Xplore digital library database system to retrieve relevant visualization education literature based on keyword search.

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Section: Rationalementioning

confidence: 99%

Digital library keyword analysis for visualization education research

Bratt

2018

JARHE

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“…This system assists users in navigating relevant information based on their past preferences and provides personalized suggestions for various library resources. These recommendations encompass a wide range of recommendations including book (Mo et al, 2019), research article/paper (Magara et al, 2017), journal (Ghosal et al, 2019), audio (Roy et al, 2020), and many more within digital libraries.…”

Section: Introductionmentioning

confidence: 99%

Mapping the research landscape of recommender systems for digital libraries

Awal,

Tehlan

2024

RLJ

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Background of the study: In today’s information-rich environment, researchers face the difficulty of managing information overload and struggle to identify meaningful information among a plethora of choices. A recommender system plays a crucial role in assisting users in discovering relevant information from a digital library. Purpose: This study aims to provide a bibliometric analysis of research publications on recommender systems for digital libraries. Method: The research method involved the quantitative bibliometric approach to analyse the research publications from the Web of Science and Scopus databases using Biblioshiny. The dataset retrieved comprises 374 documents published between the period of 2004-2023. Findings: The study's findings highlight that the number of publications was notably high in 2009 and 2018 within the analyzed period. The most significant contributors are Porcel C, Herrera-Viedma E, and Beel J. There is a considerable international collaboration between the countries- China, USA, and Germany. Conclusions: This study indicates an emerging interest in recommender systems for digital libraries with the continuous evolution of new recommendation models. There is a huge potential for research considering the availability of multimodal data, the continuous evolution of new technologies such as deep learning, and opportunities for the development of an architecture for integrating different digital libraries.

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