Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network
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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.
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