通过预训练 Edge Transformer 实现在线游戏中的好友排序
Friend Ranking in Online Games via Pre-training Edge Transformers
AI总结：
本文将在线游戏好友召回建模为链接预测问题，提出利用双边玩家特征与历史事件的 Edge Transformer，并通过掩码自编码器预训练，在三款腾讯游戏的离线及在线 A/B 测试中取得最优效果。
摘要：
好友召回是提升在线游戏 Daily Active Users（DAU，日活跃用户）的重要途径。该问题本质上是要生成一个合适的流失好友排序列表。传统好友召回方法关注好友亲密度等规则，或训练分类器来预测流失玩家的回归概率，但忽略了（活跃）玩家的特征信息以及历史好友召回事件。在本工作中，我们将好友召回视为一个链接预测问题，并探索了若干链接预测方法，这些方法能够同时利用活跃玩家与流失玩家的特征以及历史事件。此外，我们提出了一种新颖的 Edge Transformer 模型，并通过掩码自编码器对该模型进行预训练。我们的方法在三款腾讯游戏的离线实验和在线 A/B 测试中均取得了 state-of-the-art（最先进）结果。
英文摘要：
Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
