9,966 research outputs found
Recruitment Market Trend Analysis with Sequential Latent Variable Models
Recruitment market analysis provides valuable understanding of
industry-specific economic growth and plays an important role for both
employers and job seekers. With the rapid development of online recruitment
services, massive recruitment data have been accumulated and enable a new
paradigm for recruitment market analysis. However, traditional methods for
recruitment market analysis largely rely on the knowledge of domain experts and
classic statistical models, which are usually too general to model large-scale
dynamic recruitment data, and have difficulties to capture the fine-grained
market trends. To this end, in this paper, we propose a new research paradigm
for recruitment market analysis by leveraging unsupervised learning techniques
for automatically discovering recruitment market trends based on large-scale
recruitment data. Specifically, we develop a novel sequential latent variable
model, named MTLVM, which is designed for capturing the sequential dependencies
of corporate recruitment states and is able to automatically learn the latent
recruitment topics within a Bayesian generative framework. In particular, to
capture the variability of recruitment topics over time, we design hierarchical
dirichlet processes for MTLVM. These processes allow to dynamically generate
the evolving recruitment topics. Finally, we implement a prototype system to
empirically evaluate our approach based on real-world recruitment data in
China. Indeed, by visualizing the results from MTLVM, we can successfully
reveal many interesting findings, such as the popularity of LBS related jobs
reached the peak in the 2nd half of 2014, and decreased in 2015.Comment: 11 pages, 30 figure, SIGKDD 201
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
The Role of Trust in Explaining Food Choice: Combining Choice Experiment and Attribute BestâWorst Scaling
This paper presents empirical findings from a combination of two elicitation techniquesâdiscrete choice experiment (DCE) and bestâworst scaling (BWS)âto provide information about the role of consumersâ trust in food choice decisions in the case of credence attributes. The analysis was based on a sample of 459 Taiwanese consumers and focuses on red sweet peppers. DCE data were examined using latent class analysis to investigate the importance and the utility different consumer segments attach to the production method, country of origin, and chemical residue testing. The relevance of attitudinal and trust-based items was identified by BWS using a hierarchical Bayesian mixed logit model and was aggregated to five latent components by means of principal component analysis. Applying a multinomial logit model, participantsâ latent class membership (obtained from DCE data) was regressed on the identified attitudinal and trust components, as well as demographic information. Results of the DCE latent class analysis for the product attributes show that four segments may be distinguished. Linking the DCE with the attitudinal dimensions reveals that consumersâ attitude and trust significantly explain class membership and therefore, consumersâ preferences for different credence attributes. Based on our results, we derive recommendations for industry and policy
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
The wide spread use of online recruitment services has led to information
explosion in the job market. As a result, the recruiters have to seek the
intelligent ways for Person Job Fit, which is the bridge for adapting the right
job seekers to the right positions. Existing studies on Person Job Fit have a
focus on measuring the matching degree between the talent qualification and the
job requirements mainly based on the manual inspection of human resource
experts despite of the subjective, incomplete, and inefficient nature of the
human judgement. To this end, in this paper, we propose a novel end to end
Ability aware Person Job Fit Neural Network model, which has a goal of reducing
the dependence on manual labour and can provide better interpretation about the
fitting results. The key idea is to exploit the rich information available at
abundant historical job application data. Specifically, we propose a word level
semantic representation for both job requirements and job seekers' experiences
based on Recurrent Neural Network. Along this line, four hierarchical ability
aware attention strategies are designed to measure the different importance of
job requirements for semantic representation, as well as measuring the
different contribution of each job experience to a specific ability
requirement. Finally, extensive experiments on a large scale real world data
set clearly validate the effectiveness and interpretability of the APJFNN
framework compared with several baselines.Comment: This is an extended version of our SIGIR18 pape
Mind the Gap! Consumer Perceptions and Choices of Medicare Part D Prescription Drug Plans
Medicare Part D provides prescription drug coverage through Medicare approved plans offered by private insurance companies and HMOs. In this paper, we study the role of current prescription drug use and health risks, related expectations, and subjective factors in the demand for prescription drug insurance. To characterize rational behavior in the complex Part D environment, we develop an intertemporal optimization model of enrollment decisions. We generally find that seniors' choices respond to the incentives provided by their own health status and the market environment as predicted by the optimization model. The proportion of individuals who do not attain the optimal choice is small, but the margin for error is also small since enrollment is transparently optimal for most eligible seniors. Further, there is also evidence that seniors over-react to some salient features of the choice situation, do not take full account of the future benefit and cost consequences of their decisions, or the expected net benefits and risk properties of alternative plans.
Expanding the methodological toolbox of HRM researchers:The added value of latent bathtub models and optimal matching analysis
Researchers frequently rely on general linear models (GLMs) to investigate the impact of human resource management (HRM) decisions. However, the structure of organizations and recent technological advancements in the measurement of HRM processes cause contemporary HR data to be hierarchical and/or longitudinal. At the same time, the growing interest in effects at different levels of analysis and over prolonged periods of time further drives the need for HRM researchers to differentiate from traditional methodology. While multilevel techniques have become more common, this article proposes two additional methods that may complement the current methodological toolbox of HRM researchers. Latent bathtub models can accurately describe the multilevel mechanisms occurring in organizations, even if the outcome resides at the higher level of analysis. Optimal matching analysis can be useful to unveil longitudinal patterns in HR data, particularly in contexts where HRM processes are measured on a continuous basis. Illustrating the methodsâ applicability to research on employee engagement, this paper demonstrates that the HRM communityâboth research and practiceâcan benefit from a more diversified methodological toolbox, drawing on techniques from within and outside the direct field to improve the decision-making process
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