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Item-based Collaborative Filtering (ICF) is wildly used recommendation method. Under the assumption that a user tends to choose an item similar to what she has interacted before, ICF uses items that a user has interacted to learn a user's profile, and then makes recommendations by calculating the similarity between a target item and the user's profile. Therefore, the method of learning item representations from data is crucial in ICF. However, most existing ICF methods only consider the low-order connectivity between a target item and a target user's interacted items. The consideration limits the improvement of recommendation qualities. In this paper, by using the message-passing idea of Graph Convolution Networks (GCN), we propose GCN-ICF to capture the high-order connectivity between users and items in the user-item interaction bipartite graph structure. The method enriches the representation of each item through its neighbors in the bipartite graph structure. The experimental results on three public datasets verify the highly positive effect of the performance of the GCN-ICF method.
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