AI based Book Recommender System with Hybrid Approach

Abstract

In recent years, recommender systems have become increasingly popular and are applied to a diverse range of applications, due to development of items and its various types available, that leaves the users to choose from abundant provided options. Recommendations typically speed up searches and make it easier for users to access content that they are interested in, and also surprise them with offers they would have never searched for. By using filtering methods for pre-processing the data, recommendations are provided either through collaborative filtering or through content-based filtering. In this paper, we demonstrate a recommendation model that involves Matrix Factorization as a collaborative filtering solution used for providing recommendations. And with further application of artificial intelligence over the previously obtained results from collaborative filtering, the final precise list of top recommended items is listed for the user thereby using a hybrid approach in recommendation. Thus, the recommender model provides personalised recommendations.