In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way. However, powerful negative sampling methods that is adapted to GNN-based recommenders still requires a lot of efforts. One critical gap is that it is rather tough to distinguish real negatives from massive unobserved items during hard negative sampling. Towards this problem, this paper develops a novel hard negative sampling method for GNN-based recommendation systems by simply reformulating the loss function. We conduct various experiments on three datasets, demonstrating that the method proposed outperforms a set of state-of-the-art benchmarks.
翻译:在推荐人系统中,利用图形神经网络(GNN)来制定用户和项目之间的双面关系是一种有希望的方法,然而,适应基于GNN的推荐人的强大负面抽样方法仍然需要作出大量努力。一个关键的差距是,很难区分实际的负面数据与硬性负面抽样中大量未观测到的物品。为了解决这个问题,本文件为基于GNN的建议系统开发了一种新型的硬性负面抽样方法,简单地重新配置损失功能。我们在三个数据集上进行了各种实验,表明所提议的方法优于一套最新基准。