Personalized recommendation service of social media based on collaborative filtering and gene map

Abstract

With the popularity of social media, personalized recommendation services have become increasingly important. However, traditional collaborative filtering recommendation algorithms face many challenges when dealing with social media data. To improve the accuracy and efficiency of recommendation, this paper presents a collaborative filtering recommendation service in view of social media gene map. This study creates a social media genetic map by analyzing social media data and extracting user interests and behavioral characteristics. On this basis, a collaborative filtering recommendation model is constructed that takes into account the social network, content and historical behavior of users. In the performance testing of recommendation models, the research methods were compared with collaborative filtering algorithms based on alternating least squares, collaborative filtering algorithms based on generative game neural networks, and singular value decomposition algorithms. In model training, the model constructed in this study was superior to the other three types of algorithms in convergence speed and maximum accuracy. In the recommendation testing of three resources, the constructed recommendation model showed the best performance. Through experimental verification, the method has shown excellent outcomes in terms of recommendation accuracy and timeliness, providing an effective solution for social media recommendation services.