Browsing by Author "Shaikym A."

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    A Career Path Recommendation System For Computer Science Students
    (Faculty of Engineering and Natural Science, 2024) Shaikym A.
    This thesis presents the design, implementation, and evaluation of the Hybrid Career Path Recommendation System (HCPR), a sophisticated tool tailored specifically for guiding computer science students in their career decisions. The HCPR system innovatively combines Content-Based Filtering (CBF) and Collaborative Filtering (CF) methods into a hybrid model to enhance the accuracy and personalization of job recommendations. This integration addresses the inherent limitations of using either approach in isolation and leverages their combined strengths to improve recommendation quality. The system utilizes a comprehensive dataset that includes detailed user profiles from Stack Overflow and job postings from LinkedIn. The CBF component analyzes user profiles to match students with jobs that align with their skills and educational backgrounds, while the CF component predicts user preferences based on historical interaction patterns, enhancing the system’s ability to recommend jobs that users are likely to find appealing. The HCPR system’s performance is rigorously evaluated using precision, recall, F1-score, and ranking metrics such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). The results demonstrate a significant improvement in recommendation accuracy and user satisfaction compared to standalone filtering approaches. The theoretical contributions of this thesis include advancements in hybrid recommendation system methodologies and a novel application of these systems to career guidance for computer science students. Practically, the HCPR system provides actionable insights that help students navigate the complex job market, potentially improving educational and career outcomes. This thesis concludes with suggestions for future research, emphasizing the potential for further refinement of the system and its adaptation to other fields beyond computer science. This work contributes to the fields of educational technology and recommender systems by demonstrating how integrated data-driven approaches can be effectively applied to personal and professional development tools.