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

Salience and Market-aware Skill Extraction for Job Targeting

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based \emph{salience and market-agnostic} skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present \model, our deployed \emph{salience and market-aware} skill extraction system. The proposed \model ~shows promising results in improving the online performance of job recommendation (JYMBII) ($+1.92\%$ job apply) and skill suggestions for job posters ($-37\%$ suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed \model~from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the \model ~online to extract job targeting skills for all $20$M job postings served at LinkedIn.Comment: 9 pages, to appear in KDD202

The future of work: Towards a progressive agenda for all. EPC Issue Paper 9 DECEMBER 2019

Europe’s labour markets and the world of work in general are being transformed by the megatrends of globalisation, the fragmentation of the production and value chain, demographic ageing, new societal aspirations and the digitalisation of the economy. This Issue Paper presents the findings and policy recommendations of “The future of work – Towards a progressive agenda for all”, a European Policy Centre research project. Its main objectives were to expand public knowledge about these profound changes and to reverse the negative narrative often associated with this topic. It aimed to show how human decisions and the right policies can mitigate upcoming disruptions and provide European and national policymakers with a comprehensive toolkit for a progressive agenda for the new world of work

Taking a lifecycle approach: redefining women returners to science, engineering and technology

Measures to support women to return to the science, engineering and technology (SET) labour market have been implemented over the past three decades in response to the overall shortage of SET skills, as well as with the aim of (re)empowering individual women through their improved financial independence and labour market participation. Yet their needs remain poorly analysed and the impact of labour market reintegration measures appears to have been patchy. This paper examines the experiences of women re-entering the SET labour market after a break from employment in the light of assumptions made about them in UK public policy, particularly related to labour market and employment. Drawing on evidence from surveys and interview data from two groups of women returners to SET we conclude that their needs are more diverse and complex than is recognised in much policy thinking and practice, and that these differ at specific points within the lifecycle. These differences include their relationships to the labour market, patterns of employment, reasons for leaving SET and obstacles to re-entry. Our conclusion is that, to respond effectively to the needs and requirements of women returners to SET, UK public policy therefore needs to be considerably more nuanced than it currently appears to be. In particular, policy needs to reflect the diversity and changing situations of women returners over the lifecycle, and needs to provide for a range of interventions that tackle different obstacles to women's return throughout their working lives. It may also be that the very term 'returners' - which tends to evoke a single episode of exit from and reentry to the labour market – will need to be revisited in future scholarly and policy frameworks on women in SET

Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsJob application’ screening is a challenging and time-consuming task to execute manually. For recruiting companies such as Landing.Jobs it poses constraints on the ability to scale the business. Some systems have been built for assisting recruiters screening applications but they tend to overlook the challenges related with natural language. On the other side, most people nowadays specially in the IT-sector use the Internet to look for jobs, however, given the huge amount of job postings online, it can be complicated for a candidate to short-list the right ones for applying to. In this work we test a collection of Machine Learning algorithms and through the usage of cross-validation we calibrate the most important hyper-parameters of each algorithm. The learning algorithms attempt to learn what makes a successful match between candidate profile and job requirements using for training historical data of selected/reject applications in the screening phase. The features we use for building our models include the similarities between the job requirements and the candidate profile in dimensions such as skills, profession, location and a set of job features which intend to capture the experience level, salary expectations, among others. In a first set of experiments, our best results emerge from the application of the Multilayer Perceptron algorithm (also known as Feed-Forward Neural Networks). After this, we improve the skills-matching feature by applying techniques for semantically embedding required/offered skills in order to tackle problems such as synonyms and typos which artificially degrade the similarity between job profile and candidate profile and degrade the overall quality of the results. Through the usage of word2vec algorithm for embedding skills and Multilayer Perceptron to learn the overall matching we obtain our best results. We believe our results could be even further improved by extending the idea of semantic embedding to other features and by finding candidates with similar job preferences with the target candidate and building upon that a richer presentation of the candidate profile. We consider that the final model we present in this work can be deployed in production as a first-level tool for doing the heavy-lifting of screening all applications, then passing the top N matches for manual inspection. Also, the results of our model can be used to complement any recommendation system in place by simply running the model encoding the profile of all candidates in the database upon any new job opening and recommend the jobs to the candidates which yield higher matching probability