Application of Recommendation Algorithm Based on Matrix Dimensionality Reduction Model in Network Information Analysis Model

Abstract

The rapid progress of communication and Internet technology has led to explosive growth of network information, and also poses a huge challenge for users when obtaining the required information. To address this issue, this study applies a recommendation algorithm based on matrix dimensionality reduction model to network information analysis models. This study provides an in-depth analysis of collaborative filtering recommendation algorithms, proposes a matrix dimensionality reduction model based on singular value decomposition, and constructs a network information analysis model to achieve accurate user behavior prediction and personalized recommendations. The results showed that the collaborative filtering recommendation algorithm converged after 5000 iterations, and continuing to iterate 10000 times would not affect the loss function value. The error stabilized below 0.5 after 800 iterations. The accuracy and recall of the network information analysis model were both above 0.9, demonstrating good performance in network information analysis. The root mean square error and mean absolute error of the constructed model were both within 0.15, indicating that it could give users with more accurate recommendations and decision support in practical applications, as well as recommended content that better met their needs. This study provides new ideas and methods for effective filtering and personalized services of online information.