Job recommendation system based on content-based filtering

Searching for jobs among the numerous listings on the internet can be quite timeintensive. Therefore, it is crucial for job recommender systems to offer highly accurate predictions that align with the user’s preferences. Our initiative employs a content-based recommendation strategy to propose suitable job opportunities to job seekers. The system takes user inputs, including skills, experience, and qualifications, and employs the content-based recommendation method. This involves using techniques like cosine similarity and Term Frequency-Inverse Document Frequency. Term Frequency-Inverse Document Frequency, a well-established Natural Language Processing technique, is harnessed to convert text into a diverse set of vectors. Subsequently, the cosine similarity metric is used to gauge the resemblance between two vectors, both for determining vector similarity and for assessing word similarity. Adopting this approach brings about enhancements in accuracy, quality, and scalability, all while keeping memory usage minimal.