IEEE Organizations related to Jobs Listings

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Conferences related to Jobs Listings

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2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.



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Most published Xplore authors for Jobs Listings

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Xplore Articles related to Jobs Listings

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Finding Fairygodboss: Helping Women Navigate the Job Market

IEEE Women in Engineering Magazine, 2018

If you are reading this magazine, then you already recognize the importance of belonging to networks such as IEEE Women in Engineering. But cultivating those networks may play an even more vital role than you realize. Georgene Huang and Romy Newman, founders of Fairygodboss, have conducted surveys and found that women both look for different things in their job search ...


Ontologies to Model User Profiles in Personalized Job Recommendation

2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2018

Personalized recommendation aims to provide results that are likely to be of interest to a particular user. Personalized recommendation is useful in the domain of job search in order to provide individuals more personalized recommendations of job listings based on their preferences. User profiles a re thus constructed based on the individual users preferences. On the other hand, user profiles ...


Bayesian multi-view models for member-job matching and personalized skill recommendations

2017 IEEE International Conference on Big Data (Big Data), 2017

Inefficiencies in the labor market such as friction in matching members to jobs and the existence of skill gaps in various sectors of the economy are considered to be major problems facing economies today. The central premise of our work is that increasing the productivity of a member of the workforce (and thereby of the economy as a whole) crucially ...



Educational Resources on Jobs Listings

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IEEE-USA E-Books

  • Finding Fairygodboss: Helping Women Navigate the Job Market

    If you are reading this magazine, then you already recognize the importance of belonging to networks such as IEEE Women in Engineering. But cultivating those networks may play an even more vital role than you realize. Georgene Huang and Romy Newman, founders of Fairygodboss, have conducted surveys and found that women both look for different things in their job search and conduct their hunt in different ways than men. "Fewer women find jobs through traditional job boards or LinkedIn," says Huang, who serves as chief executive officer (CEO) of Fairygodboss. "Instead, they're much more inclined to trust their community for leads, which is unfortunate because women tend to have smaller, weaker professional networks than their male counterparts".

  • Ontologies to Model User Profiles in Personalized Job Recommendation

    Personalized recommendation aims to provide results that are likely to be of interest to a particular user. Personalized recommendation is useful in the domain of job search in order to provide individuals more personalized recommendations of job listings based on their preferences. User profiles a re thus constructed based on the individual users preferences. On the other hand, user profiles a re helpful in improving the recommendations. In general, user profiles are structured based on the individual's preferences. User profiles can be represented in various ways, one such way is ontology which is the systematic categorization and representation of relationships between various entities within a domain. Ontologies has been widely used in the domain of e-commerce and medicine. In this paper, we use ontologies in the domain of personalized job recommendation, to model user profiles. The major objective of this paper is to provide an ontology based user profile for the domain of job recommendation. In particular, we identified suitable classes, attributes and relations that are specific to job recommendation system. In addition, we presented OWL representation of the proposed ontological model such that it can be reused by domain experts.

  • Bayesian multi-view models for member-job matching and personalized skill recommendations

    Inefficiencies in the labor market such as friction in matching members to jobs and the existence of skill gaps in various sectors of the economy are considered to be major problems facing economies today. The central premise of our work is that increasing the productivity of a member of the workforce (and thereby of the economy as a whole) crucially depends on identifying and recommending skills whose acquisition will yield the highest utility gains for that member. To this end, we develop novel unsupervised Bayesian multi-view models BayesMatch and NpBayesMatch which can match members to other similar members as well as relevant jobs using shared features. The matching step is followed by a skill recommendation step SkillR which makes demand-based skill recommendations to members. Our extensive quantitative evaluation using a rich dataset comprised of member profiles and job postings from LinkedIn suggests that skill recommendations made by our algorithm are highly correlated with skills demanded in heldout future jobs compared to those made by traditional collaborative filtering algorithms that do not utilize information about skill demand. This indicates that either members of the workforce do not have skills demanded by jobs or do not have enough information about which are the best skills to signal for competing in the labor market.



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