A Hybrid AI Recommender System for Dynamic Career Guidance

Abstract: The rapid transition from traditional, manual career counseling to dynamic, automated recommendation frameworks represents a vital imperative in modern education and workforce alignment. Conventional career guidance platforms heavily rely on static rule-based psychometric profiling (such as Holland’s RIASEC framework) or isolated machine learning classifiers. However, standalone approaches consistently suffer from critical deficiencies, notably data sparsity, severe cold-start limitations for new candidates, lack of contextual text understanding, and black-box operational opacity. This paper presents a detailed technical architecture and comprehensive research paper on a novel Hybrid AI Recommender System engineered for dynamic career path generation. By unifying supervised classification models (Random Forest, XGBoost, Support Vector Machines), transformer-based Natural Language Processing (BERT embeddings with GRU-driven attention mechanisms), Graph Neural Networks (GNNs) over domain ontologies for Explainable AI (XAI), and a Retrieval-Augmented Generation (RAG) conversational interface, the system establishes a transparent, multi-dimensional decision engine. We formalize the skill gap quantization model using Euclidean distance vector spaces, delineate the technological stack (Python, Scikit-learn, PyTorch, React, Vite, TypeScript), and evaluate the pipeline using standard empirical frameworks. Experimental benchmarks demonstrate that the hybrid approach achieves superior predictive precision (91%), recall (88%), and cold-start error reduction, bridging theoretical psychometrics with dynamic labor market demands.

Keywords: Career guidance, hybrid recommender systems, machine learning, natural language processing, explainable AI, knowledge graphs, RIASEC model, skill gap quantization, semantic matching, RAG architecture.