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@article{169567,
        author = {Samruddhi Kale and Prachiti Panchpor and Snehal Javheri},
        title = {Personalized Personality Insights and Growth Recommendations Based on User’s Interests and Behaviours.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1615-1623},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169567},
        abstract = {In order to tackle the problem of more accurately and efficiently identifying modified information in media, this study presents the Roberta-LightGBM approach framework, which combines the advantages of Roberta and LightGBM. Using a natural 
language processing (NLP) our strategy seeks to quickly detect and reduce manipulated content using a machine learning technique in LightGBM and a language processing (NLP) model in Roberta. Compared to more conventional methods like BERT, which require far larger datasets for training across a variety of applications, the use of Roberta's NLP model allows for the effective training of large datasets in a short amount of time. 
In the modern era, the fast-paced evolution of industries and career landscapes has placed a premium on the ability of individuals to understand and leverage their skills effectively. However, traditional skill assessment methods, such as standardized personality tests or fixed-question surveys, often fail to capture the full spectrum of an individual's abilities. These assessments are typically static, generalized, and not tailored to a user’s unique context or personal growth. As a result, they struggle to address the dynamic and multifaceted nature of human skills, especially those developed through hobbies and personal interests.
This research proposes an innovative AI-based adaptive skill assessment system that overcomes these limitations by employing advanced natural language processing (NLP) and machine learning techniques. The system dynamically generates personalized questions in real-time, based on a user’s input about their interests and activities. By continuously analyzing responses and adjusting content accordingly, it provides a tailored and evolving assessment experience. This adaptive approach allows the system to uncover hidden or transferable skills derived from hobbies and offer relevant feedback for both personal growth and career advancement.
The key contributions of this research include the integration of NLP models like GPT-3 and BERT for question generation, the application of machine learning algorithms to map interests to skills, and the development of a feedback engine that offers actionable insights. The system's adaptability ensures that the assessment evolves alongside the user, making it a valuable tool for lifelong learning and professional development. By addressing the limitations of traditional assessment methods, this AI-driven model has the potential to transform how individuals and organizations approach skill identification and career planning.},
        keywords = {},
        month = {November},
        }