Research on the Propagation Characteristics of Negative News Information Based on Personalized Recommendation Algorithm

Abstract

This article will focus on how to reduce the negative information in the main theme report. Negative information contains subjectivity, deviation, interference, and cancellation characteristics, which will influence the primary theme report's communication effect, interfere with the audience's interpretation of the report, and cancel out the report's positive energy. The original intention of the theme report is to promote social harmony and safeguard social justice, but the appearance of negative information makes the reported effect fail to reach the expected purpose. The concept of theme reports and negative information is defined in this work. This study examines the primary topic report's qualities, such as The Times' mainstream, good content, and strong report. In addition, the form and characteristics of negative information are also described. In this paper, a collaborative filtering recommendation method based on non-neighbor user contributions is suggested, which uses a linear fitting formula to apply the responsibilities of both neighbor and non-neighbor users to the recommendation system. The results show that the accuracy and diversity of our algorithm are better than those of traditional collaborative filtering algorithms. The diversity of several common recommendation algorithms is studied. The findings reveal that the diversity of recommendation algorithms is linked to the sparsity of data as well as the algorithm's suggestion mechanism. In general, the more scarce the data, the higher the recommendation algorithm's variety. At the same time, we also study the diversity of recommendation systems, and the results show that although the overall diversity of the system is gradually decreasing, user behavior is becoming more and more diverse.