7 research outputs found
Talent flow analytics in online professional network
Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ
JobSense: A data-driven career knowledge exploration framework and system
National Research Foundation (NRF) Singapor
On analyzing job hop behavior and talent flow networks
Singapore National Research Foundation under International Research Centre@Singapore Funding Initiativ
JobComposer: Career path optimization via multicriteria utility learning
With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.)
becoming popular on the web, people are now turning to these platforms to
create and share their professional profiles, to connect with others who share
similar professional aspirations and to explore new career opportunities. These
platforms however do not offer a long-term roadmap to guide career progression
and improve workforce employability. The career trajectories of OPN users can
serve as a reference but they are not always optimal. A career plan can also be
devised through consultation with career coaches, whose knowledge may however
be limited to a few industries. To address the above limitations, we present a
novel data-driven approach dubbed JobComposer to automate career path planning
and optimization. Its key premise is that the observed career trajectories in
OPNs may not necessarily be optimal, and can be improved by learning to
maximize the sum of payoffs attainable by following a career path. At its
heart, JobComposer features a decomposition-based multicriteria utility
learning procedure to achieve the best tradeoff among different payoff criteria
in career path planning. Extensive studies using a city state-based OPN dataset
demonstrate that JobComposer returns career paths better than other baseline
methods and the actual career paths
Talent Flow Analytics in Online Professional Network
Analyzing job hopping behavior is important for understanding job preference
and career progression of working individuals. When analyzed at the workforce
population level, job hop analysis helps to gain insights of talent flow among
different jobs and organizations. Traditionally, surveys are conducted on job
seekers and employers to study job hop behavior. Beyond surveys, job hop
behavior can also be studied in a highly scalable and timely manner using a
data driven approach in response to fast-changing job landscape. Fortunately,
the advent of online professional networks (OPNs) has made it possible to
perform a large-scale analysis of talent flow. In this paper, we present a new
data analytics framework to analyze the talent flow patterns of close to 1
million working professionals from three different countries/regions using
their publicly-accessible profiles in an established OPN. As OPN data are
originally generated for professional networking applications, our proposed
framework re-purposes the same data for a different analytics task. Prior to
performing job hop analysis, we devise a job title normalization procedure to
mitigate the amount of noise in the OPN data. We then devise several metrics to
measure the amount of work experience required to take up a job, to determine
that existence duration of the job (also known as the job age), and the
correlation between the above metric and propensity of hopping. We also study
how job hop behavior is related to job promotion/demotion. Lastly, we perform
connectivity analysis at job and organization levels to derive insights on
talent flow as well as job and organizational competitiveness.Comment: arXiv admin note: extension of arXiv:1711.05887, Data Science and
Engineering, 201
One-class order embedding for dependency relation prediction
National Research Foundation (NRF) Singapor