Improving accuracy of recommendation system using neighbourhood-similarity prediction with noise correction

Recommender systems are crucial in e-commerce, analysing user preferences for personalised recommendations. As technology advances, RSs are expected to become more sophisticated, but they face challenges like human errors and natural noise. All this natural noise (NN) data has an implicit impact on RS because users' personal preferences and behaviours contribute to natural variations or noise in the rating process. Removing the NN information may result in the loss of potentially valuable data and deviations in prediction results, which are major problems that need to be addressed. In this paper, we propose a method named 'RS-NSP', which combines noise correction (NC) and neighbourhood-similarity prediction (NSP) to manage NN in recommendation systems. The NC initially performs a classification of ratings and later suggests a correction methodology. The corrected rating is recommended based on the NSP technique, where neighbourhood intersection is performed with a similarity measure by characterising items and users. Experiment evaluation with the Movielens-100K dataset shows smaller MAE and RMSE. The result shows an increase in the F1 value measure by 6.7%, suggesting a significant improvement in recommendation quality due to effective prediction and correction of noise ratings.