On the Noise Resilience of Ranking Measures

Author(s):  
Daniel Berrar
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carol Elaine Cuthbert ◽  
F. Owen Skae

PurposeThis paper explores the institutional and economic drivers of employability, as existing literature focuses on the individual and skills aspects, of employability. Tertiary institutions, possessing a strong academic reputation and standing amongst potential employers, will achieve high graduate employability, however when measured, this is not the case.Design/methodology/approachThis exploratory study builds on Santos' career boundary theory, recognising organisational boundaries; those related to the labour market, personal-aspects and finally, cultural boundaries (Santos, 2020). 37 Universities that provided their employability rate, within 12 months of graduation for 2020, are analysed. The Quacquarelli Symonds (QS) Ranking, measures drivers in terms of institutional reputation through survey responses, and partnerships with employers via research and placement data.FindingsThe regression explained 19% of the variation between the number of graduates being employed and the institutional and economic drivers. Universities in the same economic context, do not have the same number of employed students. Equally, those universities with the most favourable academic reputation, do not have the most employed student rate.Research limitations/implicationsOnly 37 universities provided all their employability data, thus, research with a larger sample will have to be conducted, but equally more needs to be done to establish why the smaller universities are unable to submit all the required data.Originality/valueAn exploratory understanding of the institutional and economic drivers of employability, is provided.


Author(s):  
Wookey Lee ◽  
Myung-Keun Shin ◽  
Soon Young Huh ◽  
Donghyun Park ◽  
Jumi Kim

Approximate Query Answering is important for incorporating knowledge abstraction and query relaxation in terms of the categorical and the numerical data. By exploiting the knowledge hierarchy, a novel method is addressed to quantify the semantic distances between the categorical information as well as the numerical data. Regarding that, an efficient query relaxation algorithm is devised to modify the approximate queries to ordinary queries based on the knowledge hierarchy. Then the ranking measures work very efficiently to cope with various combinations of complex queries with respect to the number of nodes in the hierarchy as well as the corresponding cost model.


2021 ◽  
Vol 104 (2) ◽  
Author(s):  
Enrico Fontana ◽  
Nathan Fitzpatrick ◽  
David Muñoz Ramo ◽  
Ross Duncan ◽  
Ivan Rungger

2020 ◽  
Vol 22 (4) ◽  
pp. 043006 ◽  
Author(s):  
Kunal Sharma ◽  
Sumeet Khatri ◽  
M Cerezo ◽  
Patrick J Coles
Keyword(s):  

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 183 ◽  
Author(s):  
Flora Amato ◽  
Giovanni Cozzolino ◽  
Giancarlo Sperlì

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach’s effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on.


2020 ◽  
Vol 102 (5) ◽  
Author(s):  
Lisa Hänggli ◽  
Margret Heinze ◽  
Robert König
Keyword(s):  

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