Most modern successful recommender systems are based on matrix factorization techniques, i.e., learning a latent embedding for each user and each item from the given rating matrix and use the embeddings to complete the matrix. However, these learned latent embeddings are inherently transductive and are not designed to generalize to unseen users/items or new tasks. In this paper, we aim to learn an inductive model for recommender systems based on the local graph patterns around user-item pairs. The inductive model can generalize to unseen nodes/items, and potentially also transfer to other tasks. To learn such a model, we extract a local enclosing subgraph for each training (user, item) pair, and feed the subgraphs to a graph neural network (GNN) to train a rating prediction model. We show that our model achieves highly competitive performance with state-of-the-art transductive methods, and is more stable when the rating matrix is sparse. Furthermore, our transfer learning experiment validates that the learned model is transferrable to new tasks.

本研究提出了基于图神经网络的矩阵补全算法，能够在没有用户或电影的年龄、种类等附加信息的情况下，通过在评分矩阵上提取“用户-电影”的子图来预测缺失的评分数据并取得竞争力相当的性能，而且其模型可以推广到训练期间没有出现的用户和电影上，并可用于其他相关任务，该方法证明了不需要过多的附加信息来建模推荐系统。

基于图神经网络的归纳矩阵补全