Research on CNN Recommender System Based on Movie Ratings
Dingling Wu · 2023
The rapid advancement of the Internet has rendered recommendation systems indispensable in managing extensive volumes of data. This study aims to compare the performance and impact of Convolutional Neural Network (CNN) and Singular Value Decomposition (SVD). The choice of a recommendation system is an important factor in both system performance and user satisfaction. Therefore, the selection of an appropriate algorithm for various applications is crucial. It involves the selection and processing of appropriate data sets for recommendation systems. Subsequently, experimental analysis and case studies are conducted to independently construct and evaluate recommendation systems based on CNN and SVD methodologies. The recommendation system utilizing CNN demonstrates superior performance in certain dimensions. The research additionally considers strategies for enhancing experimental design in order to optimize performance, enhance user satisfaction, and provide accurate comparative analysis. The main focus of this study is CNN model, with SVD as the comparative method. We attempted to adjust the results and parameters of the CNN network model to improve its predictive ability. The validation was conducted on the Ml-1m dataset, and it was determined that the optimized CNN network model performed significantly better than the SVD model. This study offers significant insights into the process of selecting recommendation system algorithms, thereby establishing a foundation for future research and optimization efforts.