Research and Implementation of Digital Media Recommendation System Based on Semantic Classification

Abstract

In order to study the recommendation system of digital media based on semantic classification, the CF-LFMC algorithm based on semantic classification is proposed. Firstly, the traditional algorithm is analyzed. Aiming at some problems existing in the traditional algorithm, a clustering algorithm model based on term meaning and collaborative filtering algorithm is designed by combining the collaborative filtering algorithm and project-based clustering algorithm. Before analyzing sparse data, the cold start and timeliness of the traditional algorithm are improved. Secondly, the performance comparison of three cosine similarity calculation methods of experimental IBCF algorithm, the performance comparison between CF-LFMC algorithm and IBCF algorithm, and the performance comparison between CF-LFMC algorithm and CF-LFMC algorithm without the time function is carried out. The clustering value N = 10 in the CF-LFMC algorithm is taken as the experimental result; MAE values of both algorithms decrease with the increase of the nearest neighbor number k. When the number of nearest neighbors is small, MAE values of the two algorithms are close to each other. As the number of nearest neighbors increases, the accuracy of the algorithm does not improve significantly, and the calculation cost of the algorithm will increase with the increase of the number of nearest neighbors, so the number of nearest neighbors between 20 and 30 is more appropriate. CF-LFMC shows better accuracy, and the CF-LFMC algorithm improved by the time function has improved the accuracy, which is better than the traditional algorithm in accuracy.