2017年Thomas等人提出的GCN[3]是其中的代表作之一。图2为图结构至单层GCN公式的演化,其中和分别为加入自环的邻接矩阵及节点度矩阵,为图节点特征矩阵,为GCN模型的可训练参数,为激活函数(例如ReLU),为图节点特征经过单层GCN网络后的输出特征。GCN从整图的角度出发,打通了原始图结构和神经网络之间的壁垒,但巨大的计算量使其难以应用到大规模场景中。相比之下,GraphSAGE[4]从图上节点的角度,提出了基于采样的消息传递范式,使得图神经网络在大规模图上的高效计算变得可行。GraphSAGE中的SAGE指 SAmple and aggreGatE,即采样和聚合。下图3展示了GraphSAGE的采样聚合过程。图中左侧展示了对节点A使用两层采样器采样其一阶和二阶邻居,图中右侧展示了将采样得到的一阶二阶邻居的特征通过对应的聚合函数进行聚合,得到节点A的表征,进而可以使用A的表征计算包括节点分类、链接预测及图分类在内的多种图相关的任务。
a. 同场景反馈数据稀疏:传统序列行为建模方案依赖用户在同场景的反馈数据构造正负样本进行模型训练,但用户在推荐广告场景的交互行为比较稀疏,据统计超过一半的活跃用户在近90天内无广告点击行为,超过40%的广告商品在近一个月没有被点击。如何解决反馈数据稀疏导致的用户兴趣刻画不准确、长尾商品学习不充分是我们面临的一大挑战。
b. LBS业务中不同时空场景下的兴趣刻画:到店业务中,用户在不同时间、空间下的浏览行为,往往有着完全不同的偏好。例如一个用户工作日在公司附近,可能感兴趣的就是一次方便的工作餐;在假期的家中,则会想找一个有趣的遛娃去处。但传统的图神经网络缺乏对用户请求时间和所处位置的实时感知能力。因此如何从图蕴含的丰富信息中挖掘出匹配当前时空场景的候选集合,同样是一大挑战。
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