Media Personalized Recommendation System Based on Network Algorithm

With information overload, a new feature of the Internet, a large amount of irrelevant, useless and redundant information on Weibo seriously interferes with people's accurate selection of required information. Users need to accurately and quickly obtain the marketing content they need through the media personalized recommendation system. This paper designs a media personalized recommendation system based on network algorithm, discusses the functional requirements of media personalized recommendation and the classification of personalized recommendation system based on network algorithm. Through the experimental test of the media personalized recommendation system, taking Weibo as an example, the data ratio between the number of effective recommendations and several users in each category is tested. The test results show that the correspondence rate between the information recommendation results and user interests of the personalized recommendation system studied in this paper is 79.7%, which indicates that the interest analysis of the user model of the media personalized recommendation system designed in this paper is more accurate. The validity of the algorithm is verified.