Analyzing of Personalized Recommendation Model of Social Network Users Based on Big Data

Aiming at the problem that the traditional social network user interest personalized recommendation model is affected by noise and human factors, which leads to poor recommendation effect, a social network user interest personalized recommendation model based on big data is designed. Analysis the social network users interested in constructing the theoretical basis of personalized recommendation model, analysis of the recommended model the interaction between the model and the surrounding, partitioning server network deployment module, network structure, operation model design through graphs model to recommend task allocation to the distributed computer c1uster, in order to build the user interest personalized recommendation model, using big data double association rules and data mining technology, obtain users interested in network data, through the recommendation results determine the degree of users interested in recommendations, improve recommendation effect, Through experimental comparison, it can be seen that the accuracy of personalized recommendation using this method can reach a maximum of 98%, and the practicability is strong (Abstract).