Job Recommendation System using LinkedIn User Profiles

Abstract

The rapid expansion of professional networking platforms has created vast opportunities for personalized career guidance. LinkedIn provides rich user data including skills, education, work experience, endorsements, and professional interests. This project proposes the design and implementation of a Job Recommendation System that leverages LinkedIn user profiles to suggest relevant career opportunities. The system integrates Natural Language Processing (NLP) and machine learning algorithms to analyze user attributes such as skills, qualifications, and professional history. By employing collaborative filtering and content-based recommendation techniques, the framework matches user profiles with job postings from multiple sources. The proposed system reduces the time and effort required by job seekers in identifying suitable opportunities while assisting recruiters in targeting the right candidates. Experimental evaluation demonstrates that the recommendation system improves accuracy and relevance compared to traditional keyword-based search methods, thereby enhancing the overall job search experience.

Introduction

The Job Recommendation System is an AI-driven platform designed to help job seekers discover career opportunities that align with their skills, experience, and career goals. Traditional keyword-based job searches are often inefficient because they depend on exact terminology and fail to capture the complex relationships between skills, industries, and career progression. To address these limitations, the proposed system utilizes LinkedIn profile data, Natural Language Processing (NLP), and machine learning techniques to generate personalized and intelligent job recommendations.

The system employs a hybrid recommendation approach that combines content-based filtering and collaborative filtering. Content-based filtering matches user profile attributes, such as skills, education, and work experience, with job requirements, while collaborative filtering identifies patterns among users with similar professional backgrounds and career trajectories. Additionally, the system incorporates career progression modeling, enabling recommendations that consider both current qualifications and future career development.

The architecture consists of four layers: Data Acquisition, Profile Processing, Recommendation Engine, and Presentation Layer. LinkedIn profiles and job postings are collected through APIs, while NLP techniques extract and standardize skills, technologies, and industry information from unstructured text. Machine learning models then generate compatibility scores using feature engineering, similarity analysis, matrix factorization, and hybrid ensemble methods.

The recommendation engine uses techniques such as cosine similarity, Alternating Least Squares (ALS) optimization, transformer-based embeddings, and Principal Component Analysis (PCA) to improve recommendation accuracy and scalability. The system also provides users with recommendation explanations, match-score breakdowns, and skill-gap analysis to support career development.

Experimental evaluation demonstrated strong performance, achieving a Precision@10 of 0.74, Recall@10 of 0.68, and a Mean Average Precision (MAP) of 0.71, significantly outperforming traditional keyword-based search methods. NLP-based skill extraction achieved 89% accuracy, while ontology mapping successfully standardized 94% of skill variations. User feedback indicated high satisfaction with recommendation transparency and actionable career guidance.

Conclusion

This paper presented the design, implementation, and evaluation of a Job Recommendation System leveraging LinkedIn user profiles to deliver personalized career guidance. The proposed system integrates Natural Language Processing and machine learning techniques within a hybrid recommendation architecture, combining collaborative filtering and content-based approaches to achieve superior recommendation accuracy compared to traditional keyword-based methods. The system architecture integrates LinkedIn API data acquisition, NLP-based profile and job description processing, hybrid collaborative and content-based recommendation models, and an interpretable presentation layer providing recommendation rationale and skill gap analysis. Experimental evaluation demonstrated significant improvements in recommendation precision, recall, and relevance over baseline methods, confirming the effectiveness of the proposed hybrid approach. The system demonstrates that intelligent integration of rich professional profile data with advanced recommendation algorithms can substantially reduce the time and cognitive burden associated with job searching while simultaneously enhancing recruiter efficiency in candidate identification. Prospective development directions include incorporation of real-time labor market trend analysis, integration of salary benchmarking data, development of career path simulation capabilities, and extension to additional professional networking platforms to broaden data coverage and recommendation diversity. VII. ACKNOWLEDGMENT The authors express sincere gratitude to the Department of Computer Applications, Aditya University, Surampalem, for providing the necessary support and resources to conduct this research. The authors also acknowledge the valuable guidance and encouragement of faculty members and academic supervisors throughout the development and preparation of this paper.

References

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Copyright

Copyright © 2026 Sridharapu Venkata Swamy, Chinthakula Jagadeesh Kumar, Kailash Praduman Yadav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.