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.
