Deep Learning-Enhanced Hybrid Recommender Systems for Dynamic E-Commerce Platforms - Chentao Lin; Rodziah Latih (Malikussaleh University, Aceh, Indonesia, 2025)
The success of current e-commerce relies on exact and varied recommendations which understand user context to enhance both user satisfaction and engagement levels. This research creates a deep learning-enhanced hybrid recommender system (DL-EHRS), which represents a deep learning-enhanced combination of recommendation systems specifically designed to operate in dynamic e-commerce environments. The proposed model connects Neural Collaborative Filtering (NCF) to Collaborative Filtering (CF) while using Deep Neural Networks (DNNs) together with Content-Based Filtering (CBF) to tackle existing recommendation system shortcomings. The performance benchmark of the DL-EHRS resulted in superior results than baseline models during all evaluation assessments. The recommendations produced through this system achieved high-quality performance at 98.1% accuracy, along with 97.9% precision and 97.8% recall and 97.9% F1-score. The proposed algorithm showed better processing speed than CF, CBF, and NCF because it completed operations in 0.9 seconds on average while readying real-time applications. The fast and stable training process of the model with minimum residual error proved its learning efficiency and ability to generalise through error convergence analysis. The proposed system meets user needs through a combination of latent factor learning techniques, content similarity analysis, along temporal context examination in its recommendation process. The integrated framework shows broad compatibility in online shopping environments because it produces precise predictions and deals with sparse data while generating better interfaces for users.