Book Recommendation System

Abstract

In this age of information, it is very difficult to find the right information from the enormous amount of data present in the online platforms. Recommendation system sorts through massive amounts of data to identify interest of users and makes the information search easier. In this paper, we have presented a model for a web-based personalized hybrid book recommendations system which exploits varied aspects of giving recommendations apart from the regular collaborative and content-based filtering approaches. Temporal aspects for the recommendations are incorporated. Also, for users of different age, country and their interests, personalized recommendation can be made on these demographic parameters. We are taking some information from user while signup which help to get more appropriate recommendations based on individual user interest and thus an attempt to overcome cold start problem. Three types of scenarios are covered in this paper viz. if user is new then recommendations are made depending upon user interests, second is recommendations based on past purchase history and the last is recommendation by using different algorithms namely K Nearest Neighbor (KNN), Singular Value Decomposition (SVD), Restricted Boltzmann Machines (RBM) and cosine similarity. It reduces dependency of rating-based system.