Apache Mahout based Book Recommendation System
Abhishek Verma1, V Nallarasan2

1Abhishek Verma*, Department of Information Technology, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India. 
2V Nallarasan, Assistant Professor, Department of Information Technology, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India. 
Manuscript received on September 08, 2021. | Revised Manuscript received on February 11, 2022. | Manuscript published on February 28, 2022. | PP: 110-113 | Volume-11 Issue-3, February 2022. | Retrieval Number: 100.1/ijeat.A31331011121 | DOI: 10.35940/ijeat.A3133.0211322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: E-Commerce websites plays an important role in an individual’s life as it serves as the medium for online shopping with a huge audience. With the commencement of the pandemic due to novel coronavirus, the involvement of E-Commerce websites for shopping has drastically increased or more precisely it remains as the only medium to shop. With the increasing demand for online shopping on E-Commerce websites, the role of the Recommendation System has also become vital as it accomplishes the goal to make Personalized Recommendation for users. In this paper, we set out Apache Mahout-based Book Recommendation System to help recommend books to users. With this paper, we have described our project that recommends books to users on the basis of the user’s prior experience of purchase. The platform utilizing this recommendation system is developed using Spring Framework as a part of our project. The dataset used in the process is taken from Kaggle. Dataset has ratings for various books given by users. As a part of the User-based Collaborative Filtering recommendation technique, Euclidean Distance Similarity is used as a similarity measure along with Nearest N User Neighborhood and Generic User-Based Recommender to give quality results as compared to the existing system. To get the best quality recommendation we have obtained an evaluation score of 0.5 for Euclidean Distance Similarity. 
Keywords: Apache Mahout, Book Recommendation, Collaborative Filtering, Machine Learning, Spring Framework, Web Application.
Scope of the Article: Machine Learning