In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user’s potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.

中文翻译：

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基于知识图谱的邻域聚合协同过滤

近年来，将知识图谱与推荐系统相结合的研究引起了广泛关注。通过研究知识图谱中的相互联系，可以发现用户和物品之间的潜在联系，这为物品的推荐提供了丰富的补充信息。然而，现有的大多数研究并没有有效地建立实体和用户之间的关系。因此，推荐结果可能会受到一些不相关的实体的影响。在本文中，我们提出了一种基于知识图谱的邻域聚合协同过滤（NACF）。它利用知识图谱来传播和提取用户的潜在兴趣，并迭代地将其注入到具有注意力偏差的用户特征中。我们在三个公共数据集上进行了大量实验；