In the user targeting and expanding of new shows on a video platform, the key
point is how their embeddings are generated. It's supposed to be personalized
from the perspective of both users and shows. Furthermore, the pursue of both
instant (click) and long-time (view time) rewards, and the cold-start problem
for new shows bring additional challenges. Such a problem is suitable for
processing by heterogeneous graph models, because of the natural graph
structure of data. But real-world networks usually have billions of nodes and
various types of edges. Few existing methods focus on handling large-scale data
and exploiting different types of edges, especially the latter. In this paper,
we propose a two-stage audience expansion scheme based on an edge-prompted
heterogeneous graph network which can take different double-sided interactions
and features into account. In the offline stage, to construct the graph, user
IDs and specific side information combinations of the shows are chosen to be
the nodes, and click/co-click relations and view time are used to build the
edges. Embeddings and clustered user groups are then calculated. When new shows
arrive, their embeddings and subsequent matching users can be produced within a
consistent space. In the online stage, posterior data including click/view
users are employed as seeds to look for similar users. The results on the
public datasets and our billion-scale data demonstrate the accuracy and
efficiency of our approach.

本研究提出了一种基于异构图网络的双边交互和特征组合的用户个性化嵌入方法，用于视频平台上新节目的推广，能有效解决新节目推广中的冷启动和点击率等问题。

基于边缘触发异构图网络的多个节目发行的观众扩展

Audience Expansion for Multi-show Release Based on an Edge-prompted  Heterogeneous Graph Network

Human-object interaction(HOI) detection is an important task for
understanding human activity. Graph structure is appropriate to denote the HOIs
in the scene. Since there is an subordination between human and object---human
play subjective role and object play objective role in HOI, the relations
between homogeneous entities and heterogeneous entities in the scene should
also not be equally the same. However, previous graph models regard human and
object as the same kind of nodes and do not consider that the messages are not
equally the same between different entities. In this work, we address such a
problem for HOI task by proposing a heterogeneous graph network that models
humans and objects as different kinds of nodes and incorporates intra-class
messages between homogeneous nodes and inter-class messages between
heterogeneous nodes. In addition, a graph attention mechanism based on the
intra-class context and inter-class context is exploited to improve the
learning. Extensive experiments on the benchmark datasets V-COCO and HICO-DET
demonstrate that the intra-class and inter-class messages are very important in
HOI detection and verify the effectiveness of our method.

本文提出一种基于异构图网络的目标与人交互检测方法，通过将人和物体建模为不同种类的节点，利用节点之间的内部关联信息和跨类别信息，再利用图注意力机制提升学习效果，实验表明内部关系和跨类别信息对于目标与人交互检测非常重要，并证实了模型的有效性。