An Intelligent Hybrid Recommendation Framework for Personalized Job Matching Using LinkedIn User Profiles

The rapid growth of online professional networking platforms has transformed modern recruitment by enabling organizations and job seekers to connect through data-driven digital ecosystems. Among these platforms, LinkedIn has become one of the most widely used professional networks, providing users with opportunities to showcase their educational qualifications, technical skills, work experience, certifications, and career interests. However, the enormous volume of available job postings often makes it difficult for users to identify employment opportunities that accurately match their professional profiles. Conventional search-based recruitment methods generally require considerable manual effort and frequently produce recommendations that are either irrelevant or insufficiently personalized. Consequently, there is an increasing demand for intelligent job recommendation systems capable of delivering accurate, personalized, and unbiased career suggestions through advanced recommendation algorithms. This study presents an intelligent hybrid job recommendation framework that utilizes LinkedIn user profiles to generate personalized employment recommendations. The proposed system combines Content-Based Filtering and Collaborative Filtering to exploit both user-specific profile information and collective behavioral patterns. The content-based module analyzes attributes such as educational background, professional skills, work experience, job preferences, and profile characteristics to identify employment opportunities with similar requirements. Simultaneously, the collaborative filtering component predicts user preferences by learning interactions among users and job postings through matrix factorization and optimization techniques. To further improve recommendation quality, neural network-based content representation and TensorFlow-based optimization are incorporated for effective feature learning and personalized ranking. The recommendation framework is evaluated using a diverse LinkedIn job recommendation dataset containing multiple job domains and user profiles. Performance analysis is conducted using standard evaluation metrics, including Precision, Recall, and F1-Score, to compare the effectiveness of collaborative filtering and content-based recommendation approaches. Experimental results demonstrate that the content-based filtering approach consistently achieves higher precision and recall than collaborative filtering across different job categories, indicating its superior capability for generating personalized recommendations. Furthermore, the application of dimensionality reduction techniques significantly decreases computational complexity and training time, making the proposed framework more suitable for real-time online recommendation services. The developed system demonstrates the effectiveness of integrating hybrid recommendation techniques with machine learning to provide intelligent, scalable, and personalized job recommendations, thereby improving the recruitment process for both job seekers and employers while supporting fair and efficient career matching.