A good understanding of player preferences is crucial for increasing content relevancy, especially in mobile games. This paper illustrates the use of attentive models for producing item recommendations in a mobile game scenario. The methodology comprises a combination of supervised and unsupervised approaches to create user-level recommendations while introducing a novel scale-invariant approach to the prediction. The methodology is subsequently applied to a bundle recommendation in Candy Crush Saga. The strategy of deployment, maintenance, and monitoring of ML models that are scaled up to serve millions of users is presented, along with the best practices and design patterns adopted to minimize technical debt typical of ML systems. The recommendation approach is evaluated both offline and online, with a focus on understanding the increase in engagement, click- and take rates, novelty effects, recommendation diversity, and the impact of degenerate feedback loops. We have demonstrated that the recommendation enhances user engagement by 30% concerning click rate and by more than 40% concerning take rate. In addition, we empirically quantify the diminishing effects of recommendation accuracy on user engagement.

翻译：深入理解玩家偏好对于提升内容相关性至关重要，尤其在移动游戏领域。本文阐述了在移动游戏场景中应用注意力模型生成物品推荐的方法。该方法结合监督与无监督学习路径，构建用户层级推荐，同时引入一种新颖的尺度不变预测方法。随后，将该方法应用于《糖果粉碎传奇》的捆绑推荐场景。文中详述了扩展至服务数百万用户的机器学习模型的部署、维护与监控策略，并介绍了为减少机器学习系统常见技术债务所采用的最佳实践与设计模式。通过离线与在线评估对该推荐方法进行验证，重点考察其对参与度、点击率与采纳率的提升效果、新奇效应、推荐多样性以及退化反馈循环的影响。实验表明，该推荐系统使点击率相关的用户参与度提升30%，采纳率相关的参与度提升超过40%。此外，我们通过实证量化了推荐准确度对用户参与度的边际递减效应。