A collaborative filtering recommendation algorithm based on normalization approach
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ORIGINAL RESEARCH
A collaborative filtering recommendation algorithm based on normalization approach Sanjaya Kumar Panda1 · Sourav Kumar Bhoi2 · Munesh Singh3 Received: 4 September 2019 / Accepted: 7 January 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Recommender system (RS) has grown widely in various communities over the last few years. It creates curiosity among the researchers due to the recent growth of various commerce companies, especially Flipkart and Amazon. In collaborative filtering-based RS, the system aims to provide the users with their personalized items, which is based on the users’ past history. In general, these observations are represented in the form of rating matrix. However, these ratings are not uniform as some user ratings are stringent and others are lenient. As a result, the RS is incompetent to suggest the personalized items to the stringent users. In this manuscript, we design a normalization-based collaborative filtering recommender to overcome the above problem. The proposed algorithm consists of two phases, namely designing and evaluating. In the first phase, the proposed algorithm finds the average user rating per item and counts the number of users purchased each item. Then it uses min–max normalization to find the normalized user count per item and scale the average ratings of users in a specified range. In the latter phase, the proposed algorithm divides the rating matrix into training and testing rating matrix, and predicts the users’ ratings. We perform rigorous simulations using a large variety of users and items, and compare the results with a collaborative filtering-based RS using ten performance metrics to illustrate the efficacy of the proposed algorithm. Moreover, we evaluate the results through a statistical test, t-test and 95% confidence interval. Keywords Recommender system · Collaborative filtering · Content based · Min–Max normalization · Precision · Recall
1 Introduction Over the last few years of excitement, recommender system (or recommendation system) has grown widely in various communities, such as business, commerce, education, tourism, government and many more (Bobadilla et al. 2013; Lu et al. 2015; Park 2019; Afridi et al. 2019; Nayak and Panda * Sanjaya Kumar Panda [email protected] Sourav Kumar Bhoi [email protected] Munesh Singh [email protected] 1
Department of Computer Science and Engineering, National Institute of Technology, Warangal 506004, India
2
Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur 761003, India
3
Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram 600127, India
2018; Panda et al. 2019). It is a promising platform, and creates enormous consideration among the researchers and business analyzers due to its recent attention in the commerce and web related companies, such as Flipkart, Amazon, Netflix, YouTube, LinkedIn and many more (Diaby et al. 2014; Yu e
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