Improving Recommendation Systems with Machine Learning-Based Noise Management
Kausar Attar · Communications on Applied Nonlinear Analysis · 2025
Recommendation systems have now adopted a central role in digital services in the contemporary world given its effectiveness in increasing user interest and value. However, these systems will always face the problem of handling natural noise; a situation that counterfeits the actual preferences of the users, leading to low accuracy of the recommended items. This research introduces a new framework CNN and ANN which provides an efficient way of handling natural noise to increase the efficiency of recommendations. The method that is proposed here classifies user interaction data using CNN, rejecting noise but embracing relevant inputs. ANN is then used on the denoised data for further enhancement and for generating individual product recommendations. This means that by combining CNNs ability to extract features and ANNs ability to make predictions, this two-structure model greatly decreases the chances of the system being easily fooled by noise and/or increases the accuracy of the response to true user preferences. We apply our approach with varying levels of natural noise on several datasets and obtain significant improvements over other recommendation approaches. Not only does this hybrid solution decrement the injurious repercussions of noise, it also provides suggestions for future progress of the recommendation system.