Digital Gateways to Employment: an In-Depth Analysis of Online Job Platforms

EasyChair Preprint 13075

Abstract

In this dynamically getting-employed world, technology has come up with a revolutionary process of job searching, and in the same way, dynamic interfaces [e.g., online job finding portals (OJFPs)] are developed. This content categorization integrated AIML (Artificial Intelligence and Machine Learning) is intended to make mobile and desktop experiences highly effective. It applies natural language processing through sophisticated algorithms for interpreting the preconditions and preferences of its users, giving the appropriate level of an information service to self-supporting candidates. The Online Job Finding Portal is driven by AlML that utilizes a web of coding and adaptation—it goes far beyond the standards of superficial matching, it analyzes and understands not only explicit keywords but all contextual paraphernalia intricacies in the job description and candidate profiling holistically. Such an approach delivers not only a truer job match but a fuller one since it is likely to point out more opportunities to satisfy the finer users' skills and aspirations

Keyphrases: CSS, HTML, JS, JavaScript, MongoDB, Node, React

@booklet{EasyChair:13075,
  author    = {Kirat Kaur and Abhishek Chaudhary and Gaurav Kumar Singh and Mriganka Shekhar Mukhopadhyay and Akash Kumar Singh and Adarsh Raj},
  title     = {Digital Gateways to Employment: an In-Depth Analysis of Online Job Platforms},
  howpublished = {EasyChair Preprint 13075},
  year      = {EasyChair, 2024}}