A Combined Representation Learning Approach for Better Job and Skill Recommendation

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information net- works from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims

Can Hiring Quotas Work? The Effect Of The Nitaqat Program On The Saudi Private Sector

This paper studies the effects of quota-based labor regulations on firms in the context of Saudi Arabia\u27s Nitaqat program, which imposed quotas for Saudi hiring at private firms. I use a comprehensive firm-level administrative dataset and exploit kinks in hiring incentives generated by the quotas to estimate the effects of this policy. I find that the program increased native employment at substantial cost to firms, as demonstrated by increasing exit rates and decreasing total employment at surviving firms. Firms without any Saudi employees at the onset of the program appear to bear most of these costs

Enhancing Productivity of Recruitment Process Using Data Mining & Text Mining Tools

Digital communication has significantly reduced the time it takes to send a résumé, but the recruiter’s work has become more complicated because with this technological advancement they get more résumés for each job opening. It becomes almost impossible to physically scan each résumé that meets their organization’s job requirement. The filtering and search techniques provide hundreds of résumés that can fulfill the desired criteria. Most approaches focus on either parsing the résumé to get information or propose some filtering methods. Moreover, résumés vary in format and style, making it difficult to maintain a structural repository which would contain all the necessary information. The goal of this project is to examine and propose an approach which would consider the skill sets from the potential résumés, along with expertise domains like related work experience and education, to score the selected “relevant résumé.” This approach aims at highlighting the most important and relevant résumés, thus saving an enormous amount of time and effort that is required fo

Community-Based Production of Open Source Software: What Do We Know About the Developers Who Participate?

This paper seeks to close an empirical gap regarding the motivations, personal attributes and behavioral patterns among free/libre and open source (FLOSS) developers, especially those involved in community-based production, and its findings on the existing literature and the future directions for research. Respondents to an extensive web-survey’s (FLOSS-US 2003) questions about their reasons for work on FLOSS are classified according to their distinct “motivational profiles” by hierarchical cluster analysis. Over half of them also are matched to projects of known membership sizes, revealing that although some members from each of the clusters are present in the small, medium and large ranges of the distribution of project sizes, the mixing fractions for the large and the very small project ranges are statistically different. Among developers who changed projects, there is a discernable flow from the bottom toward the very small towards to large projects, some of which is motivated by individuals seeking to improve their programming skills. It is found that the profile of early motivation, along with other individual attributes, significantly affects individual developers’ selections of projects from different regions of the size range.Open source software, FLOSS project, community-based peer production, population heterogeneity, micro-motives, motivational profiles, web-cast surveys, hierarchical cluster analysis

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

The impact of antenatal psychological group interventions on psychological well-being : a systematic review of the qualitative and quantitative evidence

Depression, anxiety and stress in the perinatal period can have serious, long-term consequences for women, their babies and their families. Over the last two decades, an increasing number of group interventions with a psychological approach have been developed to improve the psychological well-being of pregnant women. This systematic review examines interventions targeting women with elevated symptoms of, or at risk of developing, perinatal mental health problems, with the aim of understanding the successful and unsuccessful features of these interventions. We systematically searched online databases to retrieve qualitative and quantitative studies on psychological antenatal group interventions. A total number of 19 papers describing 15 studies were identified; these included interventions based on cognitive behavioural therapy, interpersonal therapy and mindfulness. Quantitative findings suggested beneficial effects in some studies, particularly for women with high baseline symptoms. However, overall there is insufficient quantitative evidence to make a general recommendation for antenatal group interventions. Qualitative findings suggest that women and their partners experience these interventions positively in terms of psychological wellbeing and providing reassurance of their ‘normality’. This review suggests that there are some benefits to attending group interventions, but further research is required to fully understand their successful and unsuccessful features

The relationship between sensory sensitivity and autistic traits in the general population.

Individuals with Autism Spectrum Disorders (ASDs) tend to have sensory processing difficulties (Baranek et al. in J Child Psychol Psychiatry 47:591–601, 2006). These difficulties include over- and under-responsiveness to sensory stimuli, and problems modulating sensory input (Ben-Sasson et al. in J Autism Dev Disorders 39:1–11, 2009). As those with ASD exist at the extreme end of a continuum of autistic traits that is also evident in the general population, we investigated the link between ASD and sensory sensitivity in the general population by administering two questionnaires online to 212 adult participants. Results showed a highly significant positive correlation (r = .775, p < .001) between number of autistic traits and the frequency of sensory processing problems. These data suggest a strong link between sensory processing and autistic traits in the general population, which in turn potentially implicates sensory processing problems in social interaction difficulties