Book Harbor: A Personalized Haven for Book Lovers Using DSA and Collaborative Filtering

Abstract

In the past few decades, recommender systems have become increasingly prevalent in our lives due to the growth of web services like YouTube, Amazon, In the past few decades, recommender systems have become increasingly prevalent in our lives due to the growth of web services like YouTube, Amazon, Netflix, and many more. Recommender systems are becoming an indispensible part of our everyday online experiences, from e-commerce (which suggests articles to buyers that might interest them) to online advertising (which suggests to users the proper contents, matching their tastes). Recommender systems, to put it broadly, are algorithms that propose relevant items to users (items could be anything from books to read to movies to watch to merchandise to buy, depending on the industry). In many businesses, recommender systems are extremely important because, when implemented well, they can bring in a substantial amount of money or serve as a means of differentiating a business from rivals. By combining sophisticated Data Structures and Algorithms (DSA), the Collaborative Filtering technique, and C++ programming, this study presents a novel methodologyto book recommendation systems. By offering highly customized book recommendations to users based on their reading habits and tastes, the proposed system hopes to improve the user experience when it comes to finding appropriate books to read. One well-known method in recommendation systems is the Collaborative Filtering algorithm, which is used to take use of the interactions and preferences of a wide range of users. Combining DSA, C++, and the Collaborative Filtering method offers a strong foundation for book recommendation systems. The system's effectiveness and personalization features overcome important drawbacks from earlier studies, making it a notable breakthrough in the field of book recommendation systems.