SARVE-2: Exploiting Social Venue Recommendation in the Context of Smart Conferences

Asabere, Nana Yaw; Xu, Bo; Acakpovi, Amevi; Deonauth, Nakema

doi:10.1109/tetc.2018.2854718

Cited by 10 publications

(12 citation statements)

References 38 publications

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“…This theme has emerged due to rapid growth in the ability of network platforms to gather and transport huge quantities of academic data in different formats from different scholarly sources (Zhou et al, 2018). Hence, scholarly big data represent millions of authors, papers, citations, conferences, and large-scale data such as author and research networks availed through ASNs (Asabere et al, 2018). The following subthemes have emerged in the study: recommendation systems, future impact assessment, information extraction, and data storage and protection, which are discussed below.…”

Section: Scholarly Big Data (Theme Iv)mentioning

confidence: 99%

“…Collaborative filtering, Content-based filtering, Contextawareness, and Hybrid are the main techniques used in developing recommendation systems and algorithms. Trust and social properties are also used for improved recommendation accuracy (Asabere et al, 2018). Generally, the academic recommendation system provides citation recommendation, collaborator recommendation, and conference recommendations.…”

Section: Recommendation Systemsmentioning

confidence: 99%

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The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

Hailu

2021

Data and Information Management

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This research provides a systematic analysis of 115 previous literatures on the use of academic social networking sites (ASNs) in scholarly communication. Previous research on the subject has mainly taken a disciplinary and user perspective. This research conceptualizes the use of ASNs in scholarly communication in the space between social interactions and the technologies themselves. Keyword analysis and scoping review approaches have been used to analyze the comprehensive literature in the field. The study found a geographic variation in what motivates academics to use ASNs. Scholar discovery and sharing are the primary driving factors identified in the literature. Four main themes within the research literature are proposed: motivation and uses, impact assessment, features and services, and scholarly big data. The study found that there has been an increase in scholarly big data research in recent years. The paper also discusses the key findings and concepts stated in each theme. This gives academics a better understanding of what ASNs can do and their weaknesses, and identifies gaps in the literature that are worth addressing in future investigations. We suggest that future studies may also extend the existing theoretical framework and epistemological approaches to better predict and clarify the socio-technical dimensions of ASNs use in scholarly communication. In addition, this study has implications for academic and research institutions, libraries and information literacy programs, and future studies on the topic.

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Section: Scholarly Big Data (Theme Iv)mentioning

confidence: 99%

Section: Recommendation Systemsmentioning

confidence: 99%

The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

Hailu

2021

Data and Information Management

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“…In order to achieve favourable and reliable experimental results, during the scientific experimentation process, two real-world datasets were interconnected and utilized, namely, HEXACO-60 dataset which is available in IEEE Data Port (doi: 10.21227/phht-pn81 ) and the ATU dataset in SARVE-2 [ 31 ].…”

Section: Performance Evaluation Of Sarppicmentioning

confidence: 99%

“…In relation to social tie data, Tables 3 and 4 illustrate the details of past and present tie strength data in [ 31 ]. The interconnected datasets were divided into 80% and 20% for the training and test sets, respectively.…”

Section: Performance Evaluation Of Sarppicmentioning

confidence: 99%

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SARPPIC: Exploiting COVID-19 Contact Tracing Recommendation through Social Awareness

Asabere

Acakpovi

Ofori

et al. 2020

Computational and Mathematical Methods in Medicine

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Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.

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Scholarly recommendation systems: a literature survey

Zhang,

Patra,

Yaseen

et al. 2023

Knowl Inf Syst

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A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.

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SARVE-2: Exploiting Social Venue Recommendation in the Context of Smart Conferences

Cited by 10 publications

References 38 publications

The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

SARPPIC: Exploiting COVID-19 Contact Tracing Recommendation through Social Awareness

Scholarly recommendation systems: a literature survey

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