IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 08, Issue 7 (July. 2018), ||V (I) || PP 01-05 International organization of Scientific Research 1 | Page Smart Book Recommendation System For Library Books: LibX Yash Trivedi, Kartik Kansaria, Prof. Deepali Vora Information Technology Vidyalankar Institute of Technology , VIT Mumbai,India Information Technology Vidyalankar Institute of Technology , VIT Mumbai,India Information Technology Vidyalankar Institute of Technology , VIT Mumbai,India Corresponding Author: Yash Trivedi Abstract— General recommendation systems are used to suggest appropriate items to the users. The book recommendation systems analyze the content of the book or reviews of readers to suggest apt choice for the user. Book recommendation systems are used to suggest a novice user with the right choice and also simplify the complex decision making process by extracting information from the knowledge base. These systems implement automation which reduce existing workload on the current organization and at the same time create a knowledge base which very useful for information extraction. LibX - An Automated Bibliotheca is basically a book recommendation system which will be used by students and staff to access the library facility such as books and papers and exam notes. It will provide the best of reference books through recommendation and feedback by others. Recommender systems help in automating and making decisions based on the collective knowledge and lay the foundation of solving decision based approaches in the future on various streams from information technology to robotics. Keywords—Recommendation Systems,Library Recommender system, Machine Learning. --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 21-06-2018 Date of acceptance: 05-07-2018 --------------------------------------------------------------------------------------------------------------------------------------- I. INTRODUCTION Recommender Systems Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict the ’rating’ or ’preference’ that user would give to an item . Recommender systems have become extremely common in recent years, and are applied in a variety of applications. Recommender systems assist and augment this natural social process to help people sift through available books. Usually, a recommender system providing fast and accurate recommendations will attract the interest of students and bring benefits to companies and organization. Usually Recommender systems produce a list of recommendations in one of three ways: Collaborative filtering (CF), Content-based filtering, and Hybrid recommender systems.[1] Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user’s likes and dislikes. Most existing recommender systems use social iterating methods that base recommendations on other use rs’ preferences. By contrast, content based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items 5 to users with unique interests and to provide explanations for its recommendations. [2] The purpose of LibX -An Automated Bibliotheca is to utilize the resources provided in the library to it’s most optimum and at the same time to create a centralized repository which helps everyone to access papers and recommends the best reference books based on the reviews by everyone a part from that it aims to spread awareness of a variety of resources provided by the library. At the same time our teachers can also load crucial exam notes and research papers required by the students. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyers interest [3]. Recommendations are being regarded as a new key measure of determining whether or not products, services and business are successful. From prior research, we know that 92 percent of all consumers report that a word-of-mouth recommendation is the leading reason they buy a product or service [4].Section 2 provides more information about different approaches to hybrid recommendation systems followed by a discussion of the proposed system in Section 3.The remaining sections deal with algorithm comparison, usage and their respective advantages/disadvantages are highlighted.