Job Recommendation System Using Deep Reinforcement Learning (DRL)

The rapid growth of online job portals and the increasing volume of job listings have made it challenging for job seekers to efficiently navigate through the vast number of available opportunities. Job recommendation systems play a crucial role in assisting users in finding relevant job opportunities based on their skills, preferences, and past experiences. This research paper proposes a job recommendation system that leverages deep learning techniques to enhance the accuracy and effectiveness of job recommendations. The system utilizes advanced algorithms to analyses user profiles, job descriptions, and historical data to generate personalized job recommendations. Experimental evaluations demonstrate the superiority of the proposed system compared to traditional recommendation methods, thereby improving the job search process for both job seekers and employers. This paper provides Job recommendation system using Deep Reinforcement Learning (DRL)

ONTOLOGY BASED TECHNICAL SKILL SIMILARITY

Online job boards have become a major platform for technical talent procurement and job search. These job portals have given rise to challenging matching and search problems. The core matching or search happens between technical skills of the job requirements and the candidate\u27s profile or keywords. The extensive list of technical skills and its polyonymous nature makes it less effective to perform a direct keyword matching. This results in substandard job matching or search results which misses out a closely matching candidate on account of it not having the exact skills. It is important to use a semantic similarity measure between skills to improve the relevance of the results. This paper proposes a semantic similarity measure between technical skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score. Feature based ontology similarity measures are used to derive a similarity score between two skills. The ontology also helps in resolving a base skill from its multiple representations. The paper discusses implementation of custom ontology, similarity measuring system and performance of the system in comparing technical skills. The proposed approach performs better than the Resumatcher system in finding the similarity between skills. Keywords

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

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

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

A Network Science and Document Similarity based Hybrid Job Recommendation System

Tööde soovitussüsteemid kasutavad erinevaid andmeallikaid lõppkasutajale parema sisu tagamiseks. Hästi toimiva soovitussüsteemi arendamine nõuab keerulisi hübriidseid lähenemisi sarnasuse kujutamisele põhinedes töökuulutuste ja resümeede sisudele ja nendevahelistele interaktsioonidele. Antud töö tulemina arendati efektiivne võrgul baseeruv töökohtade soovitussüsteem, mis kasutab Personalized PageRank algoritmi töökohtade järjestamiseks põhinedes tööotsija resümee ja töökuulutuse kui tekstiliste dokumentide sarnasustele ning eelnevatele kasutaja ja töökuulutuste vahelistele interaktsioonidele.Meie lähenemine saavutas 50%-lise saagise ja tekitas online A/B testi jooksul rohkem kandideerimisi kui eelmised algoritmid.Job recommendation systems mainly use different sources of data in order to give the better content for the end user. Developing the well-performing system requires complex hybrid approaches of representing similarity based on the content of job postings and resumes as well as interactions between them. We develop an efficient hybrid network-based job recommendation system which uses Personalized PageRank algorithm in order to rank vacancies for the users based on the similarity between resumes and job posts as textual documents, along with previous interactions of users with vacancies. Our approach achieved the recall of 50% and generated more applies for the jobs during the online A/B test than previous algorithms