Ontology-based employer demand management

Skills shortages globally pose a real and urgent need for proper investigation and workforce development planning into the future. Analysing workforce development and employer demand needs through electronic job market allows much deeper and wider research into skill shortages. Current methods do not provide the level of depth required to address such important economic implications. In this paper, we present a system aiming to gather and analyse current employer demand information from online job advertisements. It identifies current employer demand needs analysed from electronic job market

AI-Based Recruiting: The Future Ahead

The Human Resources industry is currently being revolutionized by the automation of tedious and time-consuming aspects of their processes. Since AI paradigms such as deep neural networks and other machine learning methods can make accurate predictions and analyze vast amounts of information, these technologies are suitable for facing some of the major challenges in this domain. We overview here how this industry is changing; from the automatic screening of the candidates to bias removal in most of the processes, through techniques for the automatic discovery of potential employees or new advances for improving the candidate's experience

Automatic Job Skill Taxonomy Generation For Recruitment Systems

The goal of this thesis is to optimize the job recommendation systems by automatically extracting the skills from the job descriptions. With rapid development in technology, new skills are continuously required. This makes the skill tagging of the job descriptions a more difficult problem since a simple keyword match from an already generated skill list is not suitable. A way of automatically populating the skills list to improve the job search engines is needed. This thesis focuses on solving this problem with the help of natural language processing and neural networks. Automatic detection of skills in the unstructured job description dataset is a complex problem as it involves being robust to the ambiguity of natural language and adapting to words not seen in the historical data. This thesis solves this problem by using recurrent neural network models for capturing the context of the skill words. Based on the context captured, the new system is capable of predicting if the word in the given text is a skill or not. Neural network models like Long short-term memory and Bi-directional Long short-term memory are used to capture the long term dependencies in the sentence to identify skills present in the job descriptions. Various natural language processing techniques were utilized to improve the input feature quality to the model. Results obtained from using context before and after the skill words have shown the best results in identifying skills from textual data. This can be applied to capture skills data from job ads as well as it can be extended to extract the skill features from resume data to improve the job recommendation results in the future

Decision Support System for Online Recruitment

International audienceIn the past, potential candidates for a job offer were in physical locations that could be reached through the major media that were available at the time, often strongly rooted in their local geographic space. Today, digital media replaced those traditional channels, offering advertisers a broader geographic reach. However digital channels are more and more numerous, making it difficult to target candidates on the web. Existing decision support system on e-recruitment in the literature does not identify the desired profile from a job offer (C1), the relevance of a resume (C2) or the changing environment of recruitment (C3). Thereby, the objective of our research is to optimize the e-recruitment process by designing a decision support system capable of targeting potential candidates at a lower cost and that addresses the challenges (C1), (C2) and (C3)

Matching Jobs and Resumes: a Deep Collaborative Filtering Task

International audienceThis paper tackles the automatic matching of job seekers and recruiters, based on the logs of a recruitment agency (CVs, job announcements and application clicks). Preliminary experiments reveal that good recommendation performances in collaborative filtering mode (emitting recommendations for a known recruiter using the click history) co-exist with poor performances in cold start mode (emitting recommendations based on the job announcement only). A tentative interpretation for these results is proposed, claiming that job seekers and recruiters − whose mother tongue is French − yet do not speak the same language. As first contribution, this paper shows that the information inferred from their interactions differs from the information contained in the CVs and job announcements. The second contribution is the hybrid system Majore (MAtching JObs and REsumes), where a deep neural net is trained to match the collaborative filtering representation properties. The experimental validation demonstrates Majore merits, with good matching performances in cold start mode

Local VS. Global Models for Job-Candidate Matching

RÉSUMÉ: Avec le développement des technologies de l’information et la croissance continue du marché du recrutement électronique, l’automatisation du processus de sélection pour trouver le meilleur candidat pour un poste a suscité l’intérêt des chercheurs et des ingénieurs en logiciels ce qui a conduit au développement de modèles complexes, d’algorithmes et de techniques qui exploitent le traitement du langage naturel, la similitude sémantique et l’apprentissage automatique. Cette thèse vise à compléter ce travail, en se concentrant sur la façon d’exploiter les données existantes pour améliorer les performances. Nous évaluons la notion de modèles locaux qui sont des modèles personnalisés construits dans des sous-ensembles de données connexes ayant des caractéristiques similaires. Pour l’évaluation, nous la comparons avec les modèles globaux qui sont un modèle complexe unique sans classification préalable. Pour ce faire, nous avons travaillé avec Airudi, une société de ressources humaines Française Canadienne qui nous a fourni des données réelles que nous utilisons pour construire notre cas d’étude où nous répondons aux questions de recherche suivantes : RQ1. Comment les modèles globaux se comparent-ils en performance aux modèles locaux? RQ2. Comment la précision et le rappel fonctionnent-ils sur différents seuils?----------ABSTRACT : Selecting the best candidate for a job position is a challenging topic that has been gaining interest in research and practice. This has led to increasingly more complex models, algorithms and techniques exploiting natural language processing, semantic similarity, and machine learning. This thesis complements this work by taking a step back and focusing on how to better exploit available data in order to further improve model performance. In particular, we empirically evaluate the notion of using “local” models for subsets of the data having similar characteristics (job descriptions) as opposed to using a single, complex “Global Model.” Using job candidate and description data, we found that local models perform better than the global models in terms of precision and recall, with median improvements up to 11.64%. If we substitute the under-performing models with the global model, thus creating a hybrid local model, the difference becomes significant. Our results suggest that local models for job recommendation brings performance advantages in terms of precision and recall over a global model, motivating further research in local models for job recommendation

Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

For several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases similar-to-me bias and stereotype bias in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version