Job Recommendation System

Abstract

In the realm of Job Recommendation Systems, the integration of sophisticated algorithms has revolutionized the process of connecting job seekers with ideal employment opportunities. This study delves into the design and implementation of a highly effective Job Recommendation System based on NLP and Machine learning that employs two key algorithms: TF-IDF and Cosine Similarity. Leveraging these algorithms, along with the preprocessing capabilities of the Natural Language Toolkit (NLTK) and Stopword tools, the system achieves an impressive accuracy rate of 96%. TF-IDF, a text vectorization method, transforms job descriptions and candidate profiles into numerical representations, allowing for meaningful comparisons. The Cosine Similarity algorithm quantifies the similarity between job postings and candidate profiles, facilitating precise recommendations. Preprocessing with NLTK and Stopword tools ensures that the textual data is refined and noise-free. This research underscores the significance of algorithm selection and preprocessing in enhancing the quality and relevance of job recommendations, ultimately improving the job search experience for users.