Survey on Hybrid Filtering Based Book Recommendation System

Abstract: The data available online, helps users to get information about anything of his/her interest. But since the data is huge and complex, it is difficult to get useful information from its Recommender System are effective software techniques to overcome this problem. Based on the user’s and item’s information available, these techniques provide recommendations to users in their area of interest We are using two algorithm Euclidean distance and Cosine similarity to recommend books. In future, this work can be extended to recommend books with higher performance by adding big data tools. People find it difficult searching for books based on their preferences or choices were searching is done through various sites on the internet and finally determines which book is appropriate for buying or reading or referring to, which is a very tedious job. In this project we use Artificial intelligence to provide easy access to all the people who are in search of different varieties of books by providing them with recommendations, based on their reviews, likes, preferences using hybrid-based filtering techniques. It is specially designed to collect, record, store, count and display results accurately. This machine allows users to get the best book recommendation based on their preference with high accuracy.

Keywords: Personalize, Book Recommendation, Euclidean, cosine similarity