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.

翻译：好友召回是在线游戏中提升日活跃用户数的重要手段，其核心在于生成合理的流失好友排序列表。传统的好友召回方法主要依赖亲密度规则或训练分类器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。本文将好友召回视为链路预测问题，探索了多种能够同时利用活跃玩家与流失玩家特征及历史事件的链路预测方法。进一步地，我们提出了一种新型边Transformer模型，并通过掩码自编码器进行预训练。该方法在腾讯三款游戏的离线实验和在线A/B测试中均取得了最优结果。