User Personalized Recommendation Algorithm Based on GRU Network Model in Social Networks

Abstract

Efficient and accurate personalized recommendation algorithms can effectively improve user experience satisfaction, in order to improve the performance of user personalized recommendation algorithm, this study proposes a user personalized recommendation algorithm based on deep learning network. The algorithm uses Gate Recurrent Unit (GRU) network to build the main model of personalized recommendation algorithm to reduce the influence of over fitting of multi-layer network; The attention mechanism is introduced into GRU network, so that the recommendation model can obtain the feature information of user data more accurately and reduce the influence of irrelevant data on the model; At the same time, due to the introduction of variable length mini-batch allocation method, the model training data is more complete and reliable, which can effectively improve the accuracy of user personalized recommendation. The simulation experiment is based on Amazon dataset and MovieLens dataset. The experimental results show that the proposed method has good personalized recommendation ability.