A High Accuracy Recommendation System for the Rubber Industries: Integrating Collaborative Filtering with Multi-layer Perceptron Classifier

Abstract

Abstract: Collaborative filtering is a widely used method in Machine Language to discover relationships between data. It facilitates recommendation systems that find similarities between user data and items, recommendation system playing a crucial role in various industries. Multilayer perceptron classifier used in our model, a connection with neural networks that performs well in regression and achieves high accuracy in classification tasks. When compared to other neural network architectures like convolution neural network (CNN), recurrent neural network (RNN), auto encoder (AE), and generative adversarial network (GAN), MLP remains a fundamental approach. Collaborative filtering involves multiple users, viewpoints, and data sources collaborating to classify information or patterns and recommend items that similar users might like. Instead of recommending items based on their features, we group users into neural networks with similar preferences and suggest items based on their classifier's preferences.