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@article{187533,
        author = {Ch. Tharun Kumar and V. Srilekha and T. Maanasa and M. Sree Priya and M. Kavya},
        title = {CARRER RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {6730-6732},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187533},
        abstract = {This paper proposes a novel career recommendation system that integrates Natural Language Processing (NLP) techniques powered by the SpaCy library to provide personalized career advice. The system collects detailed user inputs encompassing skills, interests, academic background, personality types, and career aspirations through a web-based interface developed with Python Flask. Utilizing semantic similarity measures between embeddings of user inputs and a structured career keyword dataset, the system overcomes limitations of rigid keyword matching by understanding the contextual meaning of natural language, including synonyms and abbreviations. The hybrid approach also incorporates rule-based filters aligning with academic streams and personality traits to refine suggestions. Experimental results demonstrate improved recommendation accuracy and user satisfaction, highlighting the system's potential as an adaptive career counseling tool.},
        keywords = {},
        month = {November},
        }