Methods for learning latent user representations from historical behavior logs have gained traction for recommendation tasks in e-commerce, content streaming, and other settings. However, this area still remains relatively underexplored in video and mobile gaming contexts. In this work, we present a novel method for overcoming this limitation by extending a long-range Transformer model from the natural language processing domain to player behavior data. We discuss specifics of behavior tracking in games and propose preprocessing and tokenization approaches by viewing in-game events in an analogous way to words in sentences, thus enabling learning player representations in a self-supervised manner in the absence of ground-truth annotations. We experimentally demonstrate the efficacy of the proposed approach in fitting the distribution of behavior events by evaluating intrinsic language modeling metrics. Furthermore, we qualitatively analyze the emerging structure of the learned embedding space and show its value for generating insights into behavior patterns to inform downstream applications.

通过将自然语言处理领域的长程Transformer模型应用到玩家行为数据中，我们提出了一种克服游戏领域中历史行为日志学习潜在用户表示的方法，在缺乏标记数据的情况下，通过将游戏中的事件与句子中的单词类比，从而实现以自我监督方式学习玩家表示，并通过评估内在语言建模指标证明了所提方法的效果，并定性分析了学习嵌入空间的新结构，并展示其在生成行为模式见解上的价值。

player2vec：基于语言模型的游戏玩家行为理解方法