International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Meta Job Recommendation System Using AI and Semantic Similarity Yash Kumar Patle¹, Yogesh Pawar², Vinay Patel³, Raj Patel´ ¹Department of Artificial Intelligence & Machine Learning, Oriental Institute of Science And Technology, Bhopal, India ²Department of Artificial Intelligence & Machine Learning, Oriental Institute of Science And Technology, Bhopal, India ³Department of Artificial Intelligence & Machine Learning, Oriental Institute of Science And Technology, Bhopal, India ⁴Department of Artificial Intelligence & Machine Learning, Oriental Institute of Science And Technology, Bhopal, India Guide: Prof. Akansha Meshram , Department of AIML, Oriental Institute of Science And Technology ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper presents a Meta Job
accurate and personalized recommendations, leading to reduced user satisfaction.
Recommendation System that provides intelligent job recommendations using Artificial Intelligence and semantic similarity techniques. The system operates on pre-collected job datasets obtained from platforms such as LinkedIn, Indeed, Naukri, and Kaggle. Job descriptions and user preferences are represented using semantic text embeddings to capture contextual relationships beyond traditional keyword matching. Facebook AI Similarity Search (FAISS) is employed to efficiently perform similarity-based retrieval between user profiles and job embeddings. The proposed approach improves recommendation relevance, scalability, and personalization while maintaining computational efficiency. The system is suitable for academic and practical applications, with future work focusing on real-time data integration and adaptive learning mechanisms.
To overcome these limitations, this paper proposes a Meta Job Recommendation System based on Artificial Intelligence and semantic similarity techniques. The system operates on pre-collected job datasets obtained from platforms such as LinkedIn, Indeed, Naukri, and Kaggle. Semantic text embeddings are generated to represent job descriptions and user preferences in a high-dimensional vector space, enabling contextual understanding of skills and requirements. Facebook AI Similarity Search (FAISS) is utilized to efficiently perform similarity matching between job embeddings and user profiles. The proposed system enhances recommendation accuracy, scalability, and personalization while remaining suitable for academic evaluation. Future work includes integrating real-time job data extraction and adaptive learning mechanisms to further improve system performance.
1. INTRODUCTION The rapid expansion of online recruitment platforms has led to an exponential increase in the number of job opportunities available to job seekers. Popular platforms such as LinkedIn, Indeed, and Naukri host millions of job postings across various industries and domains. While this growth has improved access to employment opportunities, it has also made the job search process complex and timeconsuming. Job seekers often face difficulty in identifying suitable roles that align with their skills, experience, and career preferences due to the overwhelming volume of available listings.
1.1 Background and Motivation With the increasing digitization of recruitment processes, online job portals have become the primary medium for connecting job seekers and employers. Platforms such as LinkedIn, Indeed, and Naukri continuously generate vast amounts of job-related data in the form of job descriptions, skill requirements, and role specifications. While this abundance of data provides opportunities, it also creates challenges in efficiently matching suitable candidates with relevant job openings.
Traditional job search and recommendation systems primarily rely on keyword-based filtering techniques. These approaches are limited in their ability to understand the semantic meaning of job descriptions and candidate profiles. Small variations in terminology, synonyms, or contextual differences can result in relevant job opportunities being missed or irrelevant jobs being recommended. Consequently, keyword-based systems often fail to provide
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Most existing job recommendation systems rely on traditional information retrieval techniques, such as keyword matching and rule-based filtering. These systems often fail to understand the contextual meaning of job descriptions and user skills, resulting in inaccurate or irrelevant recommendations. For example, candidates with similar skill sets but different terminology may not receive appropriate job suggestions due to rigid keyword constraints.
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