Research on the fusion mechanism of cooperative embedded filtering and crowd content recommendation

Abstract

Internet simultaneous services of large-scale users will lead to server overload and information failure. Static content recommendation system cannot adapt to the dynamic similarity characteristics of users. So, how to perceive the high accuracy of recommendation scheme in dynamic environment becomes one of the key techniques in application of educational information and embedded application. We analyze the problem of low efficiency and high error of the recommendation technology based on the user’s requirement. And, we proposed the cooperative filtering recommendation system based on the dynamic similarity of different users. In order to improve the prediction accuracy of cooperative filtering algorithm, the user’s target content would be processed with crowd scheme. Then, the system is fused with the recommendation system. According to the weights of the fusion, the crowd recommended fusion scheme are proposed. The experimental results show that the fusion mechanism of cooperative embedded filtering and crowd content recommendation has obvious advantages in terms of content recommendation accuracy, reliability, and convergence speed.