Evaluating the Scalability of Matrix Factorization and Neighborhood Based Recommender Systems

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Nikita Taneja 1 Hardeo Kumar Thakur 1,*

1. MRU/CSE/Faridabad, Haryana, 121004

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2023.01.03

Received: 29 Jul. 2022 / Revised: 7 Nov. 2022 / Accepted: 1 Dec. 2022 / Published: 8 Feb. 2023

Index Terms

Recommendation, Singular Value Decomposition (SVD), SVD++ K- Nearest Neighbor (KNN), K-Means


Recommendation Systems are everywhere, from offline shopping malls to major e-commerce websites, all use recommendation systems to enhance customer experience and grow profit. With a growing customer base, the requirement to store their interest, behavior and respond accordingly requires plenty of scalability. Thus, it is very important for companies to select a scalable recommender system, which can provide the recommendations not just accurately but with low latency as well. This paper focuses on the comparison between the four methods KMeans, KNN, SVD, and SVD++ to find out the better algorithm in terms of scalability. We have analyzed the methods on different parameters i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision, Recall and Running Time (Scalability). Results are elaborated such that selection becomes quite easy depending upon the user requirements.

Cite This Paper

Nikita Taneja, Hardeo Kumar Thakur, "Evaluating the Scalability of Matrix Factorization and Neighborhood Based Recommender Systems", International Journal of Information Technology and Computer Science(IJITCS), Vol.15, No.1, pp.21-29, 2023. DOI:10.5815/ijitcs.2023.01.03


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