A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Abstract

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

“…This system utilized user behavior information from social networks and integrated information from heterogeneous human networks. The research results indicated that this system can effectively improve recommendation systems and networks flexibility [7]. Zhu G designed a neural attention travel package recommendation system based on long and short term behavior to address current travel package recommendations not effectively meeting user preferences.…”

“…Neighborhood nodes are a collection of users who care about each other, that is, users and their friend pairs. For all users i u and user friend ( ) , i j u , the embedding vector is obtained by combining the gated network, and the expression for the embedding representation is shown in equation (7).…”

Applications of Deep Learning in News Text Classification

“…Cao et al [22] input the text features into the RCNN (region CNN) neural network model composed of RNN and CNN and applied them to text classification, and the classification performance is improved obviously. Zhao et al [23] proposed a DL model composed of CNN and LSTM based on attention (CLA) by repeated series of convolutional layers and circulation layer. Firstly, the word coding was implemented in series; secondly, they implemented it to realize the sentence coding, and finally, the implicit emotion analysis task was realized at the last layer using the softmax function, based on attention mechanism.…”