This thesis aims to utilize matrix and Natural Language Processing (NLP) techniques to provide job advice to new graduates in the USA. The motivation for this work arises from the significant layoffs that occurred in the US this past April, creating substantial challenges for recent graduates in securing employment. By focusing on Natural Language Processing (NLP), the system can analyze lengthy text passages, which facilitates the development of a more effective matching model. This approach allows for the extraction and understanding of intricate details from job descriptions and applicant profiles, ultimately leading to more accurate job recommendations. The system employs content-based filtering as its primary model, leveraging this method to analyze and match data from job descriptions and applicant profiles based on specific skills and experiences. One of the key metrics used in this content-based filtering approach is cosine similarity, which measures the cosine of the angle between two vectors—in this case, the vector representations of job descriptions and applicant profiles. The cosine similarity scores in the system range from 0.3 to 0.8. These scores indicate that the system is capable of identifying highly relevant job opportunities for some CVs, with higher scores reflecting better matches. However, for some profiles with lower scores, the recommendations might be less accurate due to insufficient or less detailed information available in the CVs. The integration of advanced NLP techniques enhances the system's ability to understand complex job descriptions and applicant profiles, leading to precise recommendations. This approach improves job suggestion relevance and accuracy, meeting diverse job seekers' needs. In summary, this job recommendation system provides recent graduates with an effective tool to navigate the U.S. job market, showcasing the potential of machine learning and NLP in addressing job recommendation challenges.
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This thesis aims to utilize matrix and Natural Language Processing (NLP) techniques to provide job advice to new graduates in the USA. The motivation for this work arises from the significant layoffs that occurred in the US this past April, creating substantial challenges for recent graduates in securing employment. By focusing on Natural Language Processing (NLP), the system can analyze lengthy text passages, which facilitates the development of a more effective matching model. This approach allows for the extraction and understanding of intricate details from job descriptions and applicant profiles, ultimately leading to more accurate job recommendations. The system employs content-based filtering as its primary model, leveraging this method to analyze and match data from job descriptions and applicant profiles based on specific skills and experiences. One of the key metrics used in this content-based filtering approach is cosine similarity, which measures the cosine of the angle between two vectors—in this case, the vector representations of job descriptions and applicant profiles. The cosine similarity scores in the system range from 0.3 to 0.8. These scores indicate that the system is capable of identifying highly relevant job opportunities for some CVs, with higher scores reflecting better matches. However, for some profiles with lower scores, the recommendations might be less accurate due to insufficient or less detailed information available in the CVs. The integration of advanced NLP techniques enhances the system's ability to understand complex job descriptions and applicant profiles, leading to precise recommendations. This approach improves job suggestion relevance and accuracy, meeting diverse job seekers' needs. In summary, this job recommendation system provides recent graduates with an effective tool to navigate the U.S. job market, showcasing the potential of machine learning and NLP in addressing job recommendation challenges.
