Friend recall is an important way to improve Daily Active Users (DAU) in
Tencent games. Traditional friend recall methods focus on rules like friend
intimacy or training a classifier for predicting lost players' return
probability, but ignore feature information of (active) players and historical
friend recall events. In this work, we treat friend recall as a link prediction
problem and explore several link prediction methods which can use features of
both active and lost players, as well as historical events. Furthermore, we
propose a novel Edge Transformer model and pre-train the model via masked
auto-encoders. Our method achieves state-of-the-art results in the offline
experiments and online A/B Tests of three Tencent games.

本研究提出了一种使用功能和历史事件信息的链接预测方法来解决腾讯游戏中的好友召回问题，并通过 Edge Transformer 模型的预训练，在三个 Tencent 游戏的离线实验和在线 A/B 测试中取得了最先进的结果。

通过预训练边缘变压器在在线游戏中进行好友召回

Friend Recall in Online Games via Pre-training Edge Transformers

Structured scene descriptions of images are useful for the automatic
processing and querying of large image databases. We show how the combination
of a semantic and a visual statistical model can improve on the task of mapping
images to their associated scene description. In this paper we consider scene
descriptions which are represented as a set of triples (subject, predicate,
object), where each triple consists of a pair of visual objects, which appear
in the image, and the relationship between them (e.g. man-riding-elephant,
man-wearing-hat). We combine a standard visual model for object detection,
based on convolutional neural networks, with a latent variable model for link
prediction. We apply multiple state-of-the-art link prediction methods and
compare their capability for visual relationship detection. One of the main
advantages of link prediction methods is that they can also generalize to
triples, which have never been observed in the training data. Our experimental
results on the recently published Stanford Visual Relationship dataset, a
challenging real world dataset, show that the integration of a semantic model
using link prediction methods can significantly improve the results for visual
relationship detection. Our combined approach achieves superior performance
compared to the state-of-the-art method from the Stanford computer vision
group.

通过将语义模型与视觉统计模型相结合，并应用多种先进的链接预测方法，本研究证明链接预测方法可以提高对于视觉关系检测的结果，并在 Stanford Visual Relationship 数据集上取得了优于当今最先进方法的性能。