Online Recruitment Services Research Articles

An Intelligent Multi-Agent System using XML for Adaptive Employment Agency Management

In recent years, e-commerce has become a significant social and cultural phenomenon. Many organizations now offer their services online. Within this context, online recruitment services play an important role in supporting individuals in job searches and assisting companies in finding personnel. Conversely, the potential that AI forecasting systems offer in distinct application fields is well known using increasingly high-performance algorithms that are particularly promising and certainly used successfully in job searches. In this field, in general, companies insert their job offers (job proposals) into a database; individuals are supported in their search for a job offer using a search engine based on classic Information Retrieval (IR) techniques. Regarding this, it is presumable that the Information Retrieval techniques currently used by recruitment services, which do not involve the use of rich user profiles, can provide a single individual with a large number of job offers, many of which are of little interest for him. This result could cause strong dissatisfaction for the user, and his renunciation of the use of such services. The Geomatics Laboratory, in the context of forecasting studies in the territorial and environmental fields, is experimenting with forecasting systems also in the business and economics fields to create a customized search engine, proposing a Recommender System developed using intelligent agents and based on XML. This system leverages detailed user profiles to assist users in personalized job offer searches, which combines classic Information Retrieval techniques with User Modeling techniques and artificial intelligence techniques. As a result, in generic domains, our system quickly achieves satisfactory results; however, it struggles to maintain this performance after many sessions. Conversely, in specialized domains, while our system requires many sessions to reach satisfactory performance initially, it consistently delivers outstanding performance once this phase is complete.

Exploring Internal and External Interactions for Semi-Structured Multivariate Attributes in Job-Resume Matching

Job-resume matching (JRM) is the core of online recruitment services for predicting the matching degree between a job post and a resume. Most of the existing methods for JRM achieve a promising performance by simplifying this task as a matching between the free-text attributes in the job post and the resume. However, they neglect the contributions of the semistructured multivariate attributes such as education and salary, which will result in an unsuccessful prediction. To address this issue, we propose a novel approach to comprehensively explore the Internal and EXternal InTeractions for semistructured multivariate attributes in JRM, i.e., InEXIT. In detail, we first encode the key and the value of each attribute as well as its source into the same semantic space. Next, to explore the complex relationships among the multivariate attributes, we propose to hierarchically model the internal interactions among the multivariate attributes inside the job post and the resume, as well as the external interactions between the job post and the resume. In particular, a stepwise fusion mechanism is designed to respectively integrate the key embeddings and the source embeddings into the value embeddings so as to clearly indicate the key and the source of the value. Finally, we employ an aggregation matching layer to predict the matching degree. We quantify the improvements of InEXIT against the competitive baselines on a real-world dataset, showing a general improvement of 4.28%, 4.10%, and 3.56% over the state-of-the-art baseline in terms of AUC, accuracy, and F1 score, respectively.

Enhancing Employer Brand Evaluation with Collaborative Topic Regression Models

Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers’ information from their employees’ online ratings and comments, which enables EBE from an employee’s perspective. To this end, in this article, we propose a method named Company Profiling–based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes--based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-the-art baselines and enable a comprehensive understanding of EBE.

An Enhanced Neural Network Approach to Person-Job Fit in Talent Recruitment

The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end T opic-based A bility-aware P erson- J ob F it N eural N etwork (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in 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 (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.

Online Recruitment: A Cognitive Perspective of Job Seekers in Nepal

IT adoption is more than just technology deployment; it requires careful consideration of social-cognitive factors. With this premise, in this paper, the behavioral intention of job seekers to use online recruitment services in Nepalese context has been examined on the basis of five determinants: Performance expectancy, Effort expectancy, Subjective Norms, Objective Norms and Facilitating conditions. The impact of predictor on outcome variable is examined using multiple regression analysis. The unified theory of acceptance and use of technology (UTAUT) has been used as basic foundation of the study.

Online Recruitment: A Cognitive Perspective of Job Seekers in Nepal

IT adoption is more than just technology deployment; it requires careful consideration of social-cognitive factors. With this premise, in this paper, the behavioral intention of job seekers to use online recruitment services in Nepalese context has been examined on the basis of five determinants: Performance expectancy, Effort expectancy, Subjective Norms, Objective Norms and Facilitating conditions. The impact of predictor on outcome variable is examined using multiple regression analysis. The unified theory of acceptance and use of technology (UTAUT) has been used as basic foundation of the study.

Google Search (Shopping) as a Precedent for Other Sectors - the Example of Google for Jobs

With its Google Search (Shopping) decision of 27 June 2017 the European Commission set the legal framework for the assessment of any favouring of Google-own services on its general search results pages. Using the example of the recent launch of Google for Jobs in Europe, this article examines the scope of this precedent. To this end, the authors (i) summarise the finding of the Google Search (Shopping) decision, (ii) describe the functioning, launch and economic consequences of Google for Jobs in Europe and (iii) analyse this launch from a competition law perspective. The article concludes that Google for Jobs is an online recruitment service akin to a job board or job search aggregator that is separate from Google’s general search service. Google’s favouring of Google for Jobs on its general search results pages as compared to competing services is capable of foreclosing the competition on the market for online recruitment services. Thus, Google’s conduct constitutes an abuse of dominance under the legal framework set by the Google Search (Shopping) decision.

A Case Study on the Personalized Online Recruitment Services : Focusing on Worldjob+’s Use of Splunk

A Case Study on the Personalized Online Recruitment Services : Focusing on Worldjob+’s Use of Splunk

Using a crowdsourcing platform to collect data for your PhD research

Recruiting participants for research can be an arduous process. However, with the advent of online recruitment services, called crowdsourcing platforms, recruitment and research can be conducted quicker than ever before. I have used one crowdsourcing platform, Prolific (until recently called Prolific Academic), to conduct my PhD research. In this article I have gathered some hints and tips for using a crowdsourcing platform to conduct research based on my experience of using Prolific.

Online recruitment services: another playground for fraudsters

It is increasingly common for someone actively seeking a job online to come across appealing but fake ads offering high wages, flexible hours, teleworking and career growth opportunities. Usually, job ads with such favourable conditions are examples of Online Recruitment Fraud (ORF) which attempt to collect unsuspecting candidates’ personal information. ORF is a relatively new field of variable severity that can escalate quickly to extensive scams. Corporate hiring has recently been ported to the cloud. However, this has fuelled a new type of online scam that jeopardises job seekers’ privacy and harms the reputation of organisations. Employment scams share common characteristics with others, such as email spam and phishing, but also have their own peculiarities. Sokratis Vidros and Georgios Kambourakis of the University of the Aegean, Greece and Constantinos Kolias of George Mason University, US present a preliminary analysis of real-life fraudulent job ads.

Internet use in clinical trials

Recruiting patients is a critical point of today's clinical research and, along the years, several solutions have been proposed, even if their efficacy seems to be doubtful. On the other hand, nowadays, Internet represents a great opportunity for improving clinical trial recruitments. Nevertheless, on-line recruitment services (e-recruitment) could ensure some advantages (such as facilitating interaction between supply and demand of clinical research, time and money savings/optimizations, data entry errors reduction), but also raise some issues (such as those related to sampling, information, consent, real identity of participants and risks for data breaches). The article debates on the difficulties to recruit patients for clinical research, in general, and e-recruitment particularly, discussing some ethical issues raised by internet enrolment.

An XML-Based Multiagent System for Supporting Online Recruitment Services

In this paper, we propose an Extensible Markup Language (XML)-based multiagent recommender system for supporting online recruitment services. Our system is characterized by the following features: 1) it handles user profiles for personalizing the job search over the Internet; 2) it is based on the intelligent agent technology; and 3) it uses XML for guaranteeing a light, versatile, and standard mechanism for information representation, storing, and exchange. This paper discusses the basic features of the proposed system, presents the results of an experimental study we have carried out for evaluating its performance, and makes a comparison between the proposed system and other e-recruitment systems already presented in the past.

Fixed fee online recruitment services for engineering industry

Fixed fee online recruitment services for engineering industry

Personalization techniques for online recruitment services

Personalization techniques for online recruitment services