Friend Ranking in Online Games via Pre-training Edge Transformers
This work addresses the challenge of improving Daily Active Users (DAU) for online game platforms by enhancing friend recall ranking, representing an incremental advancement in applying link prediction methods to this domain.
The paper tackled the problem of friend recall in online games by framing it as a link prediction task and proposing a novel Edge Transformer model pre-trained with masked auto-encoders, achieving state-of-the-art results in offline experiments and online A/B tests across three Tencent games.
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.