  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.
