Unexpected interest recommender system with graph neural network

Abstract

Traditional recommender systems often face the filter bubble problem when they focus on recommending familiar items to users. The over-specialized recommended contents will make users bored. To solve this problem, researchers have proposed models that focus on unexpectedness, but these models all suffer from incomplete learning of features. To address this problem, we propose an unexpected interest recommender system with graph neural network (UIRS-GNN). First, we preprocess the input data with a graph convolutional network. It enriches user and item feature vectors by aggregating neighborhood information. Second, we transform the GRU and propose the attention-based long short-term gated recurrent unit network to learn user preferences hidden in historical behavior sequences. Then, we input the preprocessed feature vectors of users and items into the unexpected interest model, and solve the problem of insufficient feature information learning by aggregating neighborhood information. Furthermore, our model also alleviates data sparsity due to our deep learning feature information. Finally, empirical evaluations with several competitive baseline models on three real-world datasets reveal the superior performance of UIRS-GNN.