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.

传统的推荐系统在专注于向用户推荐熟悉的项目时，往往会面临过滤气泡问题。过度专业化的推荐内容会让用户感到厌烦。为了解决这个问题，研究人员提出了关注意外性的模型，但这些模型都存在特征学习不完全的问题。为了解决这个问题，我们提出了一个带有图神经网络（UIRS-GNN）的意想不到的兴趣推荐系统。首先，我们使用图卷积网络对输入数据进行预处理。它通过聚合邻域信息来丰富用户和项目特征向量。其次，我们对 GRU 进行改造，提出基于注意力的长短期门控循环单元网络来学习隐藏在历史行为序列中的用户偏好。然后，我们将预处理后的用户和物品的特征向量输入到意外兴趣模型中，通过聚合邻域信息来解决特征信息学习不足的问题。此外，由于我们的深度学习特征信息，我们的模型还减轻了数据稀疏性。最后，对三个真实世界数据集的几个竞争基线模型的经验评估揭示了 UIRS-GNN 的卓越性能。