Research on personalized recommendation model of social network based on random walk algorithm

Abstract

With the vigorous development of the social Internet, users have paid extensive attention to recommendation technology. In the face of the cold start problem of the existing introduction methods in the Internet and the situation of the Internet information content where users are not fully considered, the personalized recommendation formula using graph entropy in the trust Internet was generated. First of all, the user's item graph is created according to the user's item and the content of information transferred between them, and the user trust graph is created by importing the trust mechanism; Then, the initial similarity between the new user and the product is obtained by using the random walk algorithm for the two images, or the new user product similarity is obtained based on the trust relationship; Then repeat the random walk process until the convergence value of similarity is stable; Then, the two groups of similarity rates can be weighted by the graph entropy on UIG and UTG, and the final selection list of target customers can be obtained accordingly. However, according to the actual sample set of Epings and Film Trust, the accuracy of RWAGE has increased by 34.7% and 19.4% respectively compared with the typical random walk algorithm, and the recall success rate has also increased by 28.9% and 21.1% respectively, which can significantly reduce the recommended cold start time and is higher than the comparison in accuracy and coverage.