A recommendation system is one of the top applications of data science. Every consumer Internet company requires a recommendation system like Netflix, Youtube, a news feed, etc. What you want to show out of a huge range of items is a recommendation system.
A recommendation engine is a class of machine learning which offers relevant suggestions to the customer. Before the recommendation system, the major tendency to buy was to take a suggestion from friends. But Now Google knows what news you will read, Youtube knows what type of videos you will watch based on your search history, watch history, or purchase history.
A recommendation system helps an organization to create loyal customers and build trust by them desired products and services for which they came on your site. The recommendation system today are so powerful that they can handle the new customer too who has visited the site for the first time. They recommend the products which are currently trending or highly rated and they can also recommend the products which bring maximum profit to the company.
A recommendation system is usually built using 3 techniques which are content-based filtering, collaborative filtering, and a combination of both.
This repository contains the code for a book recommendation system. The system is built using the Book-Crossing dataset, which is a public dataset of book ratings and reviews.
The main objective of this project is to create a book recommendation system that can recommend books to users based on their past ratings and reviews. The system is built using a collaborative filtering approach, which means that it recommends books to users based on the ratings and reviews of other users who have similar tastes.
The Book-Crossing dataset comprises 3 files:
users.csv: This file contains information about the users, such as their user ID, location, and age. books.csv: This file contains information about the books, such as their ISBN, title, author, year of publication, and publisher. ratings.csv: This file contains the book rating information. Ratings (Book-Rating) are either explicit, expressed on a scale from 1-10 (higher values denoting higher appreciation), or implicit, expressed by 0.
The code for the book recommendation system is written in Python. The code is divided into two main parts:
The data processing code: This code cleans and preprocesses the data. The recommendation system code: This code builds and evaluates the recommendation system.
The code in this repository can be used to build your own book recommendation system. To use the code, you will need to download the Book-Crossing dataset and install the necessary Python packages.