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arXiv · 2302.10043

Friend Ranking in Online Games via Pre-training Edge Transformers

Liang Yao·Jiazhen Peng·Shenggong Ji·Qiang Liu·Hongyun Cai·Feng He·Xu Cheng
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Abstract

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.

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Liang Yao, Jiazhen Peng, Shenggong Ji, Qiang Liu, Hongyun Cai, Feng He, Xu Cheng. 2023-02-20. Friend Ranking in Online Games via Pre-training Edge Transformers. https://arxiv.org/abs/2302.10043

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