Sessionbased graph convolutional recurrent neural networks recommendation model

Abstract:

The session based recommendation is a subtask of recommendation system, which addresses the recommendation problem about anonymous users. Although the existing methods with the graph neural network for recommendation have achieved good results, which are insufficient to capture more accurate potential information in user’s sessions. To solve the above problem, a novel recommendation model, session based Graph Convolutional Recurrent Neural Networks (GCRNN) is proposed in this paper to capture more potential information in user’s sessions and enhance the recommendation effects. In the proposed model, the graph convolutional neural network layer is used to capture structural information in the user graphs, as well the recurrent neural network layer is utilized to obtain the temporal information and the dependency relationship between sessions to acquire more affluent and accurate potential information in sessions. We conducted extensive experiments on two public datasets, and the results show that GCRNN is superior to the state of the art methods in the sessionbased recommendation.