Design and Implementation of an AI-Based Career Recommendation System for Personalized Guidance

Authors:

Mr. K. Naresh¹, Chejarla Pavan kumar²

¹Assistant Professor, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India knareshcse@smec.ac.in

 ²Student, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India pmnk.04@gmail.com

Abstract

Career selection is a critical decision-making process that significantly influences an individual’s professional growth and long-term success. However, students often struggle to identify suitable career paths due to the abundance of unstructured information and the lack of personalized guidance systems. Traditional career recommendation platforms primarily rely on keyword-based search and static data representations, which limit their ability to understand user intent and provide context-aware suggestions.

This paper presents the design and implementation of an AI-based career recommendation system aimed at delivering personalized guidance through intelligent information retrieval and dynamic content generation. The system is designed using a modular architecture that integrates a frontend interface, backend services, a structured relational database, and AI-driven components.

To enhance recommendation accuracy, the system employs transformer-based embedding techniques to convert textual career data into high-dimensional vector representations. Semantic similarity between user queries and stored profession data is computed using cosine similarity, enabling context-aware retrieval of relevant career options beyond exact keyword matching. Additionally, a dynamic filtering mechanism is applied to ensure that only the most relevant recommendations are presented to the user.

To further support personalized guidance, the system incorporates a large language model (LLM) to generate structured learning paths tailored to specific professions. These learning paths include foundational concepts, required skills, tools, and project-based progression, providing users with actionable insights for career development.

The system is implemented using a full-stack approach, with an administrative interface that enables continuous updating and management of career-related data. Experimental evaluation demonstrates that the proposed system improves the relevance of career recommendations and enhances user understanding through AI-generated guidance.

The proposed approach effectively combines semantic search and generative intelligence, offering a scalable and intelligent solution for next-generation career recommendation systems focused on personalized guidance.

Keywords: Artificial Intelligence, Career Recommendation System, Semantic Search, Natural Language Processing, Personalized Guidance, Machine Learning, Large Language Models