Graph neural network recommendation algorithm based on improved dual tower model

Abstract The information explosion is strongly impacting recommendation systems. The dual tower neural network model can quickly filter and score tens of millions of items in the recall phase and assemble them into a ranking recommendation. This model has attracted considerable interest from researchers and industry. However, the user and item towers operate independently in the dual tower model, so it cannot interact with information features, and its recommendation accuracy is less-than-optimal. The application of graph neural networks to recommendation systems has developed rapidly, enabling item recommendation through a high-order learning mechanism based on feature interaction between user information and item information. This paper proposes an interactive higher-order dual tower recommendation algorithm, the Interactive Higher-order Dual Tower (IHDT) model. Our model is based on an interactive learning mechanism, which combines the user's feature information into the item encoder and the item’s feature information into the user encoder simultaneously. The resulting higher-order interaction connectivity of graph neural networks is used to obtain a better representation of users and items. Finally, the model algorithm is compared with a benchmark algorithm on four public evaluation indicators on a public dataset. The experimental results show that the model performance is superior to other algorithms. The dataset file of this paper used is available at https: //pan. baidu. com/s/1AEh-XNPₙJhxqrKuK0CgeA, with password: a2b4