DSGNN: A dynamic and static intentions integrated graph neural network for session-based recommendation

Session-based recommendation, which aims to predict subsequent user actions based on anonymous sessions, plays a significant role in many online services. Existing methods construct each session as a digraph and then capture the rich transition relationship of items by using graph neural networks. However, their ability to obtain the user's static intentions is insufficient and they suffer from improper combinations of different user intentions. In this study, we propose a dynamic and static intentions integrated graph neural network (DSGNN) for session-based recommendation, in which the user's intentions captured by a digraph and an undigraph are comprehensively considered to enhance the recommendation performance. The prediction results from the dynamic and static intentions are combined using a lightweight gating network, which reduces the conflict between the two kinds of information. Furthermore, the weighted inner product is designed to alleviate the impact of the value of the item representation vectors. Extensive experiments on three real-world datasets show that the DSGNN outperforms other state-of-the-art methods and demonstrates that the components in our model are effective. (c) 2021 Elsevier B.V. All rights reserved.