SYNERGISTIC NEURAL MATRIX FACTORIZATION: ELEVATING COMPLEMENTARY-PRODUCT RECOMMENDATIONS IN E-COMMERCE

Abstract

This research uses deep neural networks (DNNs) to improve complementary-product recommendations in e-commerce systems. Utilizing the MovieLens dataset, the study investigates the effectiveness of the Neural Collaborative Filtering (NeuMF) model, which integrates Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP) components. The study emphasizes the importance of preprocessing the model dataset to ensure data quality and relevance. Key performance metrics were analyzed to evaluate the model's performance, including model accuracy, loss, ROC curve, and precision-recall curve. Results indicate that the NeuMF model effectively captures linear and non-linear user-item interactions, improving recommendation accuracy. The findings underscore the model's potential to enhance user satisfaction and drive sales in e-commerce platforms.