BOOK RECOMMENDATION SYSTEM WEB BASED USING K-NEAREST NEIGHBORS ALGORITHM AND COLLABORATIVE FILTERING

Abstract

A recommendation system is a technology that can facilitate internet users in finding things. The recommendations given can be in the form of information, goods, and others. This system has been widely used in various platforms including social media, e-commerce, and many others. In this paper, the recommendation system made is about the recommendation system on books. There are many types of books currently, books about education, novels or fiction stories, comics, biographies, self-development and others. However, because of the many choices of books that can be read, readers will usually find it difficult to choose a book. To overcome this, readers will usually look for a recommendation through friends or through the internet. In the recommendation system there are several methods that can be used, namely content-based filtering, collaborative filtering, and hybrid filtering. Hybrid recommendation is a combination of two or more recommendation systems. In this paper the method used is collaborative filtering, the way it works isto add up the ratings or choices of a product, find user profiles by looking at the rating history given by users, then generate new recommendations based on comparisons between user patterns. The algorithm used in this book recommendation system is the K-Nearest Neighbors algorithm. The K-Nearest Neighbors algorithm is one algorithm that can provide recommendations with good accuracy. This system helps to save readers time in searching for books so that readers can make the right decision about the book to be read next, the recommendations given by the system to users are expected to be in accordance with the preferences or interests of each reader who uses this application.