Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles

Yadav, Pratyush; Pervin, Nargis

doi:10.1016/j.joi.2022.101336

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Within library and information studies (LIS), recent research considering recommender systems has focused mainly on the technical aspects and on the possibilities for recommender systems in different library settings (Collins et al, 2018;De Nart and Tasso, 2014;Jomsri, 2018;Khademizadeh et al, 2022;Rhanoui et al, 2022;Simovi c, 2018;Yadav and Pervin, 2022), rather than on their role in shaping information practices. However, some recent studies within LIS have focused on AI and algorithms, with major areas of interest being the use and role of search engines in everyday situations (Andersson, 2017(Andersson, , 2021(Andersson, , 2022Sundin, 2019, 2022b;Sundin et al, 2017) and information literacy in relation to algorithms or algorithmic literacy (Lloyd, 2019).…”

Section: Algorithmic Recommendationsmentioning

confidence: 99%

“…, 2022; Rhanoui et al. , 2022; Simović, 2018; Yadav and Pervin, 2022), rather than on their role in shaping information practices. However, some recent studies within LIS have focused on AI and algorithms, with major areas of interest being the use and role of search engines in everyday situations (Andersson, 2017, 2021, 2022; Haider and Sundin, 2019, 2022b; Sundin et al.…”

Section: Introductionmentioning

confidence: 99%

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Algorithmic recommendations enabling and constraining information practices among young people

Jylhä,

Hirvonen,

Haider

2024

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PurposeThis study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.Design/methodology/approachThematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.FindingsThe key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.Originality/valueThis study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

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Section: Algorithmic Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithmic recommendations enabling and constraining information practices among young people

Jylhä,

Hirvonen,

Haider

2024

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“…To improve relevancy in the Digital Library domain, much research into recommender systems has been undertaken [2]. However, architecturally, semantic search result relevancy…”

Section: Introductionmentioning

confidence: 99%

Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Evans,

Latifi,

Ahsan

et al. 2024

Information

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Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29%.

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Focused Issue on Digital Library Challenges to Support the Open Science Process

Di Nunzio

2023

Int J Digit Libr

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Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles

Cited by 7 publications

References 44 publications

Algorithmic recommendations enabling and constraining information practices among young people

Algorithmic recommendations enabling and constraining information practices among young people

Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Focused Issue on Digital Library Challenges to Support the Open Science Process

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