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

Discrimination in a Low-Wage Labor Market: A Field Experiment

Decades of racial progress have led some researchers and policymakers to doubt that discrimination remains an important cause of economic inequality. To study contemporary discrimination we conducted a field experiment in the low-wage labor market of New York City. The experiment recruited white, black, and Latino job applicants, called testers, who were matched on demographic characteristics and interpersonal skills. The testers were given equivalent resumes and sent to apply in tandem for hundreds of entry-level jobs. Our results show that black applicants were half as likely to receive a callback or job offer relative to equally qualified whites. In fact, black and Latino applicants with clean backgrounds fared no better than a white applicant just released from prison. Additional qualitative evidence from our testers' experiences further illustrates the multiple points at which employment trajectories can be deflected by various forms of racial bias. Together these results point to the subtle but systematic forms of discrimination that continue to shape employment opportunities for low-wage workers.race, field experiment, discrimination, labor markets

The State of Knowledge on the Role and Impact of Labour Market Information: A Survey of the International Evidence

This report provides a critical examination of the international literature on the role and impact of labour market information (LMI). The purpose of this exercise is to assess the current state of knowledge on the role and impact of LMI and to identify gaps in our knowledge. The report finds that we know very little about the impact of LMI per se on labour market outcomes. What knowledge we do possess must be inferred from evaluations of labour market programs or technologies that are related to LMI, such as job-search assistance programs, career counseling, and internet-based LMI. The literature on each of these topics reveals some beneficial impacts on labour market outcomes, but the precise role of LMI in driving these relationships is never specified.labour market, international, labour market information, decision-making, labour market outcomes

Motivation vs. relevance: Using strong ties to find a job in Urban China

While the idea that contacts matter in finding a job is intuitively appealing, we still do not know—after decades of research—how and why strong ties benefit job seekers. To resolve this confusion, we need to theorize how specific characteristics of ties are related to the mechanisms that make job search through contacts effective. We have reasons to expect that, while a contact’s motivation influences the likelihood that a job seeker receives an offer, her homophily with the job seeker on occupation and other job-relevant attributes influences the quality of the offer. The use of strong ties among university students to find jobs in China provides a unique opportunity to empirically isolate the relationship between contact characteristics and the mechanisms through which contacts benefit the job seeker. I tested my hypotheses with data on both the successful and unsuccessful job searches of 478 graduates of China’s flagship universities, who, as first-time job seekers, primarily used strong ties. Survey results are consistent with my hypotheses: job seekers who used strong ties to look for jobs had more offers—but not better offers—than those who used only formal methods.Social Science Research Council (U.S.) (International Pre-dissertation Fellowship)Social Science Research Council (U.S.) (Blakemore Fellowship for the Study of East Asian Languages

Enhancing Job Recommendation through LLM-based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness. With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful capabilities of text processing and reasoning, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for the models in generating high-quality resumes. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.Comment: 13 pages, 6 figures, 3 table

A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.Comment: 15 pages, 6 figure