The effectiveness of graphical recommender system depends on the quantity and quality of negative sampling. This paper selects some typical recommender system models, as well as some latest negative sampling strategies on the models as baseline. Based on typical graphical recommender model, we divide sample region into assigned-n areas and use AdaSim to give different weight to these areas to form positive set and negative set. Because of the volume and significance of negative items, we also proposed a subset selection model to narrow the core negative samples.

翻译：图形推荐系统的有效性取决于负采样的数量与质量。本文选取若干典型推荐系统模型及这些模型上最新采用的负采样策略作为基线方法。基于典型图形推荐模型，我们将采样区域划分为指定数量的子区域，并利用AdaSim为不同区域赋予差异化权重，以构建正采样集与负采样集。考虑到负样本的规模与重要性，我们进一步提出子集选择模型，用于缩减核心负样本的范围。