Title: An exploratory data analysis on rating data using recommender system algorithms

Authors: N Lakshmipathi Anantha; Bhanu Prakash Battula

Addresses: Acharya Nagarjuna University, Nagarjuna Nagar, Nambur, Guntur, Andhra Pradesh 522510, India; Department of Information Technology, VFSTR (Deemed to be University), Vadlamudi, Andhra Pradesh, India ' Department of Computer Science and Engineering, Thirumala Engineering College, Jonnalagadda, Narasaraopet, Andhra Pradesh, India

Abstract: Day to day, the uploading of data into the world wide web and e-commerce directed the development of recommender systems. Recommender system filters the information based on the user's interest. Nowadays, recommender systems are being used in every domain. The advantage of a recommender system is that it makes searching easy. Recommender systems are classified into content-based filtering, collaborative filtering and hybrid approach. In this paper, we analysed the performance of item similarity, matrix factorisation and popular recommender algorithms and evaluated with precision-recall and root mean square error metrics.

Keywords: recommender systems; collaborative filtering; matrix factorisation; evaluation metrics.

DOI: 10.1504/IJAIP.2024.136787

International Journal of Advanced Intelligence Paradigms, 2024 Vol.27 No.1, pp.43 - 60

Received: 21 Apr 2018
Accepted: 28 May 2018

Published online: 22 Feb 2024 *

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