AI-POWERED BOOK RECOMMENDATION FRAMEWORK USING NATURAL LANGUAGE PROCESSING

The exponential growth of digital reading platforms has resulted in the availability of millions of books online, posing a significant challenge for users in identifying content aligned with their interests. Conventional recommendation systems, primarily based on collaborative filtering and user ratings, often encounter critical limitations such as the cold-start problem and data sparsity, which adversely affect their performance and scalability.To address these challenges, this paper presents an AI-powered content-based book recommendation framework that leverages Natural Language Processing (NLP) techniques to generate accurate and meaningful recommendations using only textual descriptions of books. The proposed system employs Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for measuring semantic relationships between book descriptions.The framework incorporates comprehensive Exploratory Data Analysis (EDA) and robust text preprocessing techniques, including tokenization, stop-word removal, and normalization, to enhance data quality and representation. The processed textual data is transformed into high-dimensional vector space representations, enabling efficient similarity computation and retrieval of relevant books.Furthermore, the system integrates semantic topic modeling to improve diversity and mitigate over-specialization in recommendations. An interactive Streamlit-based web application is developed to provide real-time user interaction and recommendation