User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well wit sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance.

翻译：建议中的用户-项目互动自然可以称为用户-项目双部分图。鉴于图形神经网络(GNNS)在图表演示学习中的成功,已提出基于GNNC的C型方法来推进推荐系统。这些方法往往根据学习的用户和项目嵌入方式提出建议。然而,我们发现,它们没有运行在现实世界建议中相当常见的智能稀疏用户-项目图。因此,在这项工作中,我们引入了一种新颖的视角,以建立基于GNN的CF型方法来提出建议,从而导致拟议框架的地方化图表协作过滤(LGCF)。LGCF的一个主要优点是,它不需要为每个用户和项目学习嵌入,这种嵌入方式在少见的情景中具有挑战性。或者,LGCF的目的是将有用的CF型信息编成一个本地化图,并根据这种图表提出建议。关于各种数据集的广泛实验证实了LGCF的有效性,特别是在稀少的情景中。此外,实证结果显示,LGCF为嵌入式的CF型模型提供了补充信息,可以用来促进建议的执行。