Content based book recommendation system using textual feature analysis

• React

In today’s digital age, readers have access to an overwhelming number of books across various genres and platforms. With such vast options, it becomes increasingly difficult for users to identify books that align with their specific tastes and interests. While this provides readers with a wide selection, it also presents a challenge of finding books that align with their interests and preferences. To address this issue, recommendation systems have emerged as powerful tools for personalized book suggestions. Book recommendation systems aim to assist users in discovering relevant books based on their preferences, browsing history, and other relevant factors. Traditional recommendation systems have primarily relied on collaborative filtering techniques, which analyse user behaviour and similarities among users to generate recommendations. However, collaborative filtering can suffer from the "cold-start" problem when dealing with new or less popular books, as there may be limited user data available for accurate recommendations. To overcome these limitations, content-based filtering has gained prominence in recommendation systems. Content-based filtering focuses on the characteristics and attributes of the items themselves rather than relying solely on user behaviour. In the context of book recommendations, content-based filtering analyses various book features, such as genre, author, keywords, and synopsis, to establish user profiles and make personalized recommendations. This project focuses on building a Content Filtering System for Book Recommendations, which suggests books based on the similarity of content attributes such as genre, author, keywords, and descriptions. Unlike collaborative filtering, which relies on user behavior and ratings, content-based filtering analyzes the intrinsic features of books to generate recommendations. This approach is particularly useful when user data is limited or unavailable.