Enhancing Recommendation Systems Through a Hybrid FuzzySparse Similarity Model and Optimizing Noise Reduction

Authors

Kausar Salim Attar
Ramrao Adik Institute of Technology, D.Y.Patil Deemed to be University
Ashish Jadhav
Ramrao Adik Institute of Technology, D. Y.Patil Deemed to be University
DOI: https://doi.org/10.52549/ijeei.v13i4.6780
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Published: 22-11-2025 • Issue: Vol. 13 No. 4: December 2025 • Section: Regular paper

Abstract

The vast amount of information available on the Internet and e-commerce has led to the development of recommendation systems that help users find relevant products or content. As digital applications continue to grow worldwide, ensuring the right user experience in a short period of time remains a significant challenge. With the increasing use of mobile devices, ensuring access to accurate and timely information has become an essential part of today's business operations. The accuracy of the estimates depends not only on the methodology used but also on the accuracy of the data. External factors and unexpected noise issues can affect users in the rating process. This problematic influence, coming from well-intentioned users, can lead to distortion of the rating results during the recommendation process. In this study, we present a Hybrid Fuzzy-Sparse Similarity (HFSS) methodology designed to improve the accuracy of recommendations and reduce the error caused by source noise due to rating sparsity. Initially, a limited amount of data utilized in a form of rating matrix along with the sparse distribution is collected for the analysis of the recommendation process. An extended fuzzy set matrix creation mechanism is proposed to solve the existing sparsity problems. By using the extended sparse matrix, the similarity of models is calculated from the complex set theory, which allows for model-based recommendations. The proposed HFSS model is evaluated on the MovieLensdatabase. First, the system recommendation performance evaluation is made, and later a comparison performance is measured by MAE, RMSE, and F1 score metrics, which demonstrates better recommendation accuracy and performance than the comparing performance-based methods.
Keywords: Recommender system; Fuzzy set; Data Sparsity; Similarity measures

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Author Biographies

Kausar Salim Attar
Kausar Attar has over 16.5 years of experience in Industry and Academia. Currently, she is working as an Assistant Professor in the Computer Engineering Department. Academic Qualifications: 1) Pursuing PhD in Computer Engineering from RAIT, D.Y.Patil Deemed to be University. 2) ME in Computer Engineering from Mumbai University. 3) BE in Computer Engineering from Shivaji University.
Ashish Jadhav
Dr. Ashish has over 36 years of experience in Industry, Academia and Research. Currently he is involved in the setup of an Incubator in the University where young entrepreneurs can be mentored to succeed in their ventures and create start-ups. He is the former Head of the Department of Information Technology at the School of Engineering. He has guided PG and PhD students, and his areas of interest are Networking and Security, Coding and Cryptography, AI/ML and Deep Learning. Academic Qualifications: 1) PhD from Indian Institute of Technology, Kanpur 2) MS in Electronics and Controls from Birla Institute of Science and Technology, Pilani 3) BE in Electronics Engineering from University of Mumbai

How to Cite

[1]
K. S. Attar and A. Jadhav, “Enhancing Recommendation Systems Through a Hybrid FuzzySparse Similarity Model and Optimizing Noise Reduction”, IJEEI, vol. 13, no. 4, pp. 897–910, Nov. 2025, doi: 10.52549/ijeei.v13i4.6780.