Learning-Based Matched Representation System for Job Recommendation

Abstract

Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.

Brain Tumor Classification Using Four Versions of EfficientNet

“…By looking at the confusion matrix (Figure 8), the desired result is the highest possible True_Negative (TN) and True_Positive (TP) values, and the lowest possible False_Negative (FN) and False_Positive (FP) values. Precision is used to show the trade-off in the model between the sensitivity of detecting TP while balancing the of FPs [20], [21]. It is given by equation (1) as follows Furthermore, we can define the true positive rate (TPR) which is called recall with equation (2) [22].…”

Bidirectional recommendation in HR analytics through text summarization

“…A greedy search is evaluated at every time stage from a probability distribution 𝑦 𝑠 of 𝑝(𝑦 𝑖 𝑠 |𝑦 1 𝑠 , … 𝑦 𝑖 𝑠 , 𝑥) in every decoder's period stage and baseline outcome 𝑦 ∧ is acquired through increasing outcome probability distribution. Determining 𝑟(𝑦) to reward function for outcome sequence 𝑦 and then this is compared with actual sequence 𝑦 * , a formula is described as (13):…”

“…Every day people employ industry-scale suggestion systems to suggest 1000s of candidates to the clients and the other way around opening to candidates. The job recommendation system is enriched on a heterogeneous collection of input information, resumes of candidates, vacancy texts, and structured information [13].…”

Machine Learning-Based Ransomware Classification of Bitcoin Transactions

“…In ML and statistics, there are a variety of methods for evaluating the performance of a classifer. Te most common metrics are as follows: Applied Computational Intelligence and Soft Computing (i) Confusion matrix is the most widely used metric (see Table 3) [29,30].…”