Recommendation System Using Personalized Chat-Bot

Abstract

Predicting a person’s personality can significantly enhance personalized recommendations across various domains. This study introduces PersonalityBERT, a deep learning model designed to determine personality traits from user-generated text. We utilized the MBTI (Myers-Briggs Type Indicator) dataset, which categorizes personalities into 16 distinct types, and fine-tuned BERT (Bidirectional Encoder Representations from Transformers) to analyze text input effectively. BERT’s contextaware and transformer-based multi-head self-attention mechanisms enable a deeper understanding of semantic meaning, improving personality classification accuracy.To extract meaningful personality insights, preprocessing techniques such as stopword removal, lemmatization, and text vectorization were applied. The fine-tuned BERT model was trained on the MBTI dataset and optimized using hyperparameter tuning, including learning rate adjustments, gradient accumulation, and weight decay regularization. Our PersonalityBERT model achieved an accuracy of 88%, surpassing baseline models, including traditional NLP classifiers and LSTMs. Additionally, the model produced a precision of 0.86, recall of 0.90, F1-score of 0.88, and an AUC-ROC score of 0.94, demonstrating high reliability in personality classification. Beyond classification, this model was integrated into a personality-based recommendation system. Using Sentence Transformers and Cosine Similarity, PersonalityBERT provides personalized book recommendations based on a user’s personality type and textual input. The chatbot framework was developed using Streamlit, with backend deployment. The model successfully matches users to book reviews with high semantic relevance, ensuring enhanced recommendation accuracy. Currently, PersonalityBERT is optimized for book recommendations, but its applications can be extended to various domains, such as movie recommendations on Netflix, career guidance platforms, and AI-driven personalized learning systems. This research demonstrates that deep learning-based personality prediction models using BERT provide accurate and meaningful recommendations. Future work involves further semantic integration, cross-domain applications, and expanding the model to multilingual personality analysis to improve recommendation diversity and accuracy.