Research on library book recommendation method based on personalized recall LFM recommendation algorithm
In order to solve the problem of low recommendation accuracy in traditional book recommendation methods,this study proposes a library book recommendation method based on personalized recall latent factor model.With the improved LFM algorithm,this study constructs a book recommendation model and verifies its performance.The experimental results show that the improved algorithm achieves 97%in precision rate and 0.87 area under the accuracy-recall curve,both of which are better than the traditional comparison algorithm.In addition,by analyzing the application effect of the model,it is found that the model performs well in several aspects,such as recommendation accuracy,ease of operation,and novelty,with scores of no less than 9.0.This indicates that the proposed book recommendation method effectively improves recommendation accuracy and can better meet the personalized needs of readers.
万方数据
维普
