Enhancing Recommendation Systems Through a Hybrid FuzzySparse Similarity Model and Optimizing Noise Reduction - Attar, Kausar Salim; Jadhav, Ashish (IAES Indonesian Section, 2025)
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.