Personalized Book Recommendations: A Hybrid Approach Leveraging Collaborative Filtering, Association Rule Mining, and Content-Based Filtering

Abstract

Well over ten years already, recommender systems have been in use. Many people have perpetually grappled with synonymous with selecting what to read next. The choice of a textbook or reference book to read on a subject they are unaware of might be difficult for even students. Nowadays, people can go into a library or browse the internet without having a specific book in mind. But each reader is different, in their tastes and interests. In today's information-rich world, Essential tools like recommendation systems play a pivotal role in simplifying the lives of consumers. For book lovers, the Book Recommendation Sys- tem(BRS) is the ideal fix for readers. Online bookstores are competing for attention, but current systems extract unnecessary data and result in low user satisfaction, this author crafted the BRS, merging collaborative filtering(CF), association rule mining(arm), and content-based filtering. BRS delivers recommendations that are both efficient and effective. Concept papers primary intention encourage a love of reading and help people form lifelong habits. BRS selects an ideal book based on a reader's preferences and data from various sources, inspiring individuals to read more and discover new authors and genres. Leveraging Information sets and machine learning algorithms, collaborative filtering and content filtering techniques are used to help people find the perfect book that fascinates and incites a desire to explore additional literary treasures.