Content-based Music Similarity with Triplet Networks

We explore the feasibility of using triplet neural networks to embed songs based on content-based music similarity. Our network is trained using triplets of songs such that two songs by the same artist are embedded closer to one another than to a third song by a different artist. We compare two models that are trained using different ways of picking this third song: at random vs. based on shared genre labels. Our experiments are conducted using songs from the Free Music Archive and use standard audio features. The initial results show that shallow Siamese networks can be used to embed music for a simple artist retrieval task

A Review of Resume Analysis and Job Description Matching Using Machine Learning

In the contemporary job market, the effective matching of resumes to job descriptions is a critical facet of talent acquisition. This research paper provides a comprehensive review of the advancements, methodologies, and challenges associated with leveraging machine learning (ML) and natural language processing (NLP) techniques for resume analysis and job description matching. The study surveys the existing literature, synthesizes key findings, and presents a taxonomy of approaches employed in the field. The paper begins by elucidating the significance of efficient resume-job description matching in enhancing the recruitment process. It then delves into the foundational principles of machine learning as applied to human resource management, emphasizing the role of natural language processing, pattern recognition, and semantic analysis in extracting relevant information from resumes and job descriptions. The review encompasses an in-depth analysis of various machine learning algorithms and models utilized in resume parsing, including but not limited to neural networks, support vector machines (SVM), and ensemble methods. Moreover, the paper investigates the incorporation of deep learning architectures, such as convolutional neural networks and recurrent neural networks, for more nuanced feature extraction and representation. Key challenges and limitations associated with current methodologies are thoroughly examined, addressing issues such as the need for large, diverse datasets for robust training. The paper concludes with a discussion on future research directions and emerging trends in the realm of resume analysis and job description matching. This research contributes to the existing body of knowledge by offering a comprehensive synthesis of the current state of machine learning applications in resume analysis and job description matching, providing valuable insights for researchers, practitioners, and industry professionals seeking to optimize talent acquisition processe

Generating Natural Language Attacks in a Hard Label Black Box Setting

We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model. At each iteration, the optimization procedure allow word replacements that maximizes the overall semantic similarity between the original and the adversarial text. Further, our approach does not rely on using substitute models or any kind of training data. We demonstrate the efficacy of our proposed approach through extensive experimentation and ablation studies on five state-of-the-art target models across seven benchmark datasets. In comparison to attacks proposed in prior literature, we are able to achieve a higher success rate with lower word perturbation percentage that too in a highly restricted setting.Comment: Accepted at AAAI 2021 (Main Conference

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)

Online Reciprocal Recommendation with Theoretical Performance Guarantees

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clearvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines

Metodología para la clasificación de documentos de texto de hojas de vida basado en aprendizaje de máquina

El proceso de selección de personal es complejo y requiere una gran cantidad de información y análisis para encontrar a los candidatos adecuados para una posición. Incluye varias etapas, como la revisión de currículums, pruebas psicológicas y verificación de referencias. Sin embargo, el análisis de currículums puede ser un desafío, ya que implica una intervención humana y la gran cantidad de información puede resultar difícil de procesar por computadora. Además, las empresas pueden enfrentar dificultades y costos elevados debido a la complejidad del proceso y la alta demanda en el mercado laboral. Para resolver este problema, se propone la metodología CVNLP (Curriculum Natural Language Processing), que utiliza un conjunto de 725 hojas de vida en formatos PDF, DOCX y DOC para analizar los currículums de manera eficiente y eficaz. La metodología se aplica de manera transversal y ha demostrado su eficacia en la selección de personal. Al reducir los costos y mejorar la eficiencia en el proceso de selección de personal, las empresas pueden centrarse en su núcleo de negocio y facilitar el proceso de selección de personal. En resumen, la metodología CVNLP se presenta como una solución prometedora para mejorar la eficacia y eficiencia en los procesos de selección de personal, especialmente para las PYMEs con recursos limitado

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