Recommendation networks in human resource selection

Um dos principais desafios encontrados num processo de recrutamento de recursos humanos prende-se com a dificuldade prática de analisar de forma objetiva todos os candidatos. Neste trabalho mostra-se a aplicabilidade das redes de recomendações profissionais entre pares no âmbito de processos de recrutamento de recursos humanos. Para o efeito utilizamos a rede social LinkedIn e em particular as recomendações efetuadas por um conjunto de profissionais. Geramos uma rede de recomendações por especialidade, em que os vértices são os profissionais analisados e os arcos são as relações de recomendação. Sobre a rede de recomendações aplica-se o algoritmo PageRank para ordenar cada profissional por especialidade. A análise para várias especialidades é realizada através da avaliação multicritério, onde é aplicado o método TOPSIS.One of the main challenges in a human resources recruitment process is the difficulty of analyzing objectively all candidates. This paper shows the application of networks of professional recommendations among peers in human resources recruitment processes. For this purpose we use the LinkedIn social network and in particular the recommendations made by a subset of professionals. We generate a network of recommendations by specialty, where the vertices are the professionals and the arcs are the recommendation relationships. Based on the network of recommendations we apply the algorithm PageRank to order of each professional by specialty. The analysis for several specialties is performed using multi-criteria evaluation, where the TOPSIS method is applied.info:eu-repo/semantics/publishedVersio

Synthetic Generation of Social Network Data With Endorsements

In many simulation studies involving networks there is the need to rely on a sample network to perform the simulation experiments. In many cases, real network data is not available due to privacy concerns. In that case we can recourse to synthetic data sets with similar properties to the real data. In this paper we discuss the problem of generating synthetic data sets for a certain kind of online social network, for simulation purposes. Some popular online social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. For each particular skill, the endorsements give rise to a directed subgraph of the corresponding network, where the nodes correspond to network members or users, and the arcs represent endorsement relations. Modelling these endorsement digraphs can be done by formulating an optimization problem, which is amenable to different heuristics. Our construction method consists of two stages: The first one simulates the growth of the network, and the second one solves the aforementioned optimization problem to construct the endorsements.Comment: 5 figures, 2 algorithms, Journal of Simulation 201

Developing and validating measurement items for a multi-dimensional social network site usage construct

SNS platforms are providing simultaneously for hedonic and utilitarian type uses. However, extant research continues to model the SNS usage construct as a simplistic unidimensional construct that fails to adequately reflect the multi-dimensional nature of SNS usage in workplace contexts. This paper contributes by presenting results of a multi-phase process used to develop and validate measures of the deep structure SNS usage construct from both hedonic and utilitarian perspectives. Psychometric tests were conducted using 124 usable responses, and the results show that deep structure usage is best modelled as a reflective second order construct with three first order dimensions reflecting hedonic use, utilitarian use, and cognitive absorption. The multi-dimensional deep usage SNS usage construct will be of interest to researchers examining SNS usage in the workplace and its implications for workplace outcomes. Implications for practice, including SNS design and usage policy, are also described

O algoritmo PageRank aplicado a redes de recomendações para seleção de recursos humanos

Um dos principais desafios encontrados num processo de recrutamento de recursos humanos prende-se com a dificuldade prática de analisar de forma objetiva todos os candidatos. A grande quantidade de curricula vitae normalmente envolvida num processo deste tipo dificulta, quando não impede mesmo, uma leitura completa e, portanto, uma análise detalhada. Neste trabalho procura mostrar-se a aplicabilidade do algoritmo PageRank a redes de recomendações profissionais entre pares no âmbito de processos de recrutamento de recursos humanos. Assim, através de técnicas de data-mining, procedeu-se à extracção de dados de teste do conteúdo HTML de páginas de alguns profissionais registados no site LinkedIn, concretamente as recomendações efectuadas por cada um para um conjunto de outros profissionais por especialidade. Com os dados extraídos contruiram-se redes de recomendações por especialidade, em que os vértices são os profissionais analisados e os arcos são as relações de recomendação, redes sobre as quais se aplicou o algoritmo PageRank para classificação ordenada de cada profissional por especialidade. A extensão para várias especialidades é realizada através de um algoritmo de avaliação multicritério que, aplicado aos valores de PageRank, possibilita a classificação de um profissional tendo em conta mais do que uma especialidade.One of the main challenges encountered in a human resource recruitment process is the practical difficulty of analysing all candidates in an objective manner. The large amount of curricula vitae normally involved prevents a thorough reading and therefore a detailed analysis. This work aims to show the applicability of link-analysis techniques to networks of professional recommendations among peers in the scope of recruitment processes of human resources. So, using data-minign techniques, we proceed with the extraction of test data from the HTML content of some LinkedIn pages, moreover the recommendations made between members. This extraction allowed the construction of a network of recommendations by skill, in which the nodes are professionals and the arcs are the recommendarions. Over these networks we applied the PageRank Algorithm in order to serialize each professional by skill. The expansion to more than one skill is made through the use of an algorithm of multi-criteria evaluation, applied to the various PageRank values of each skill

Teaching Non-Technological Skills for Successful Building Information Modeling (BIM) Projects

abstract: Implementing Building Information Modeling (BIM) in construction projects has many potential benefits, but issues of projects can hinder its realization in practice. Although BIM involves using the technology, more than four-fifths of the recurring issues in current BIM-based construction projects are related to the people and processes (i.e., the non-technological elements of BIM). Therefore, in addition to the technological skills required for using BIM, educators should also prepare university graduates with the non-technological skills required for managing the people and processes of BIM. This research’s objective is to develop a learning module that teaches the non-technological skills for addressing common, people- and process-related, issues in BIM-based construction projects. To achieve this objective, this research outlines the steps taken to create the learning module and identify its impact on a BIM course. The contribution of this research is in the understanding of the pedagogical value of the developed problem-based learning module and documenting the learning module’s development process.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

O plano de relações públicas digitais para a empresa Giolaser

Nowadays, public relations assume an increasingly important role in the life of organizations, as well as the need to develop a communication plan to improve the organization's positioning in the market in which it operates and improve the relationship with its public institutions. This project aims to develop a digital public relations plan, containing a set of strategies and tactics for digital communication, having as scenario the opening of GiOlaser's first space in Portugal. The ideas, suggestions and activities developed in this project have as main goal the planning of a digital public relations strategy for the GiOlaser company. To achieve the pre-established goals, a diagnostic analysis of the company was developed, a set of digital communication tactics and techniques was defined and these were monitored and evaluated. Thus, for the company to be able to analyze the uniqueness of the Portuguese market and the possible opening of a store, presenting a differentiating service, a digital communication plan was developed, the theoretical framework, methodology, internal and external analysis, data analysis and an action plan and proposals were made.Nos dias de hoje, as relações públicas assumem cada vez mais um papel preponderante na vida das organizações, bem como a necessidade de desenvolver um plano de comunicação para melhorar o posicionamento da organização no mercado em que opera e melhorar o relacionamento com os seus públicos institucionais. Este projeto visa a realização de um plano de relações públicas digitais, contendo um conjunto de estratégias e táticas de comunicação digital, tendo como cenário a abertura do primeiro espaço da empresa GiOlaser, em Portugal. As ideias, sugestões e atividades desenvolvidas neste projeto têm como principal objetivo o planeamento de uma estratégia de relações públicas digitais para a empresa GiOlaser. Para atingir as metas pré-estabelecidas desenvolveu-se uma análise diagnóstico da empresa, definiu-se um conjunto de táticas e técnicas de comunicação digital, procedeu-se à monitorização e avaliação das mesmas. Assim, para a empresa poder analisar a singularidade do mercado português e a possível abertura de uma loja, apresentando um serviço diferenciador, desenvolveu-se um plano de comunicação digital, no qual efetuou-se o enquadramento teórico, metodologia, analise interna e externa, análise de dados e um plano de ações e pospostas

Endorsement deduction and ranking in social networks

Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users’ profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements