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@article{192792,
        author = {Prof. Vrushabh Bawankar and Prof. Atish Shriniwar and Prof. Vishal Gejge and Aditya Unune},
        title = {Conceptual Framework of Personality-Based Career and Skill Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3324-3330},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192792},
        abstract = {The increasing complexity of the modern career landscape has heightened the need for personalized guidance systems[7] capable of assisting individuals in making informed and psychologically aligned career decisions. Existing career recommendation approaches frequently rely on single-model assessments or preference-based matching, resulting in recommendations that inadequately reflect the individual’s underlying personality traits and vocational interests. To address this limitation, this paper proposes a conceptual framework that integrates the Big Five Personality Traits and Holland’s RIASEC typology to generate personalized career and skill path recommendations. 
The proposed framework consists of four major components: the Personality Profiling and Data Processing component, the Trait Interaction Matrix component, the Career–Skill Ontology component, and the Dual-Model Recommendation Engine component. These components collectively transform psychometric inputs into structured outputs comprising recommended career pathways, associated skill requirements. By incorporating two established psychological models, the framework offers a more comprehensive understanding of career–personality alignment and supports users in identifying skill gaps relevant to their recommended career trajectories. 
This study contributes a structured conceptual architecture that can serve as a foundation for future empirical validation and system development. Future research directions include expert evaluation of the framework, the incorporation of proficiency weighting for skill prioritization, and the integration of machine learning techniques to enhance recommendation accuracy.},
        keywords = {Career recommendation systems, Personality modeling, Big Five traits, RIASEC typology, Skill recommendation, Ontology-based reasoning, Hybrid recommender systems, Design science research, Psychometric integration.},
        month = {February},
        }