Recommendation System Optimization and Performance Evaluation Based on Deep Learning

Data sparsity makes it difficult for traditional collaborative filtering algorithms to find sufficient similarities, which in turn affects the accuracy and effectiveness of recommendations. To this end, this study explores the optimization and performance evaluation of deep learning-based recommendation systems, focusing on the analysis of the principles and applications of the Neural Collaborative Filtering (NCF) model. This study analyzes the framework of the NCF model and its optimization method in detail. First, The client and object encoding tensor are input into the neural network, and nonlinear transformation is performed through multiple fully connected layers to discover more intricate connections between things and users. On this basis, the study also explores how to design an optimized loss function, select appropriate embedding technology and optimization algorithm to further improve the recommendation accuracy. In addition, methods such as composite embedding and hierarchical embedding also play an important role in optimizing the recommendation effect. Experimental results show that the deep learning-based recommendation model has significant improvements in prediction accuracy, recommendation diversity and coverage compared with traditional methods.