Content-Based Book Recommender System Using Supervised Learning

Online shopping in India has already got more attention in the last few decades. The Indians are more interested in online shopping which has changed all the concepts of the market scenario. There are so many online shopping websites available nowadays like Flipkart, Amazon, Junglee, Snapdeal, etc. With the increase in the number of traders and buyers, efficient and effective techniques are used to manage the vast amount of data formed day by day. Recommender systems give a platform to filter out the given data and able to provide relevant information to the end users. The methods used in the recommender systems are content-based, collaborative-based, and demographic-based but there are some issues that fail in producing efficient information for the end users. Thus, it is very important to find the most important features that are capable of optimizing recommender systems. This paper proposed a content-based book recommender system using PCA and SVM with the help of two publicly available datasets like Amazon Book Reviews (AB) and BookCrossing (BX). The results of the proposed recommender system prove the superiority of the system.