Parallel Recommendation for Multi Interactive Resources in Mobile Networks Based on Label Attributes and Behavior Sequence

Abstract

With the rapid changes in the mobile network environment and the dynamical user's interest, existing recommendation algorithms are unable to provide resources that meet user needs, which means both the accuracy and efficiency of resource recommendation are not good enough. Therefore, this article proposes a parallel recommendation algorithm for multi interactive resource based on label attributes and behavior sequences in mobile network. The proposed method first obtains users' preferences for resources based on label attributes to increase the accuracy of recommendation; and then computes the similarity between resources to remove the redundant resources and improve recommendation efficiency. Then, Deep Convolution Generative Networks (DCGN) is used to process interaction data between multiple users and resources. Here, the input interaction behavior sequence is fed into a dual Gated Linear Unit (GLU) , and Gated Recurrent Unit (GRU) based on attention mechanism is used to extract the change of user's interest. At the same time, a feature crossover module is used to learn the target resource connection to make recommendations more relevant. Finally, a Deep Convolutional Neural Network (DCNN) is used to output the user resource interaction score to complete the resource recommendation. Experimental results show that the Normalized Discounted Cumulative Gain (NDCG) and hit rate are 0.35 and 0.18 respectively when the length of recommendation list is 8, with minimum Logloss 0.2567 and maximum Area Under the ROC Curve (AUC) 0.9157, which means the coverage rate of proposed resource recommendation is high. The resource recommendation takes 46.72 seconds to process large-scale data, which indicates that the proposed algorithm has high recommendation efficiency