针对游戏推荐中的用户行为稀疏与热门游戏垄断问题，提出生命周期表征学习方法。
Large-scale User Game Lifecycle Representation Learning
- 构建用户游戏生命周期表征（UGL）以丰富行为数据
- 离线提升广告AUC 1.83%，在线点击率增21.67%
- 适合游戏平台推荐系统研发人员参考
视频游戏产业的快速扩张要求在线游戏平台发展高效的广告与推荐系统。游戏推荐依赖于捕捉用户兴趣，但现有推荐系统中处理数十亿物品的表征学习方法不适用于游戏场景，主要因游戏数据稀疏（仅数百款游戏）和行为失衡（少数热门游戏主导）。为此，本文提出用户游戏生命周期（UGL）表征，以增强用户行为信息；设计两种行为调控策略，更好提取短期与长期兴趣；提出逆概率掩码策略应对游戏不平衡问题。离线与在线实验表明，该方法在游戏广告推荐中平均提升1.83% AUC（离线）与21.67% CVR（在线），在游戏内商品推荐中实现0.5% AUC提升与0.82% ARPU增长。
原文摘要 · Abstract (English)
The rapid expansion of video game production necessitates the development of effective advertising and recommendation systems for online game platforms. Recommending and advertising games to users hinges on capturing their interest in games. However, existing representation learning methods crafted for handling billions of items in recommendation systems are unsuitable for game advertising and recommendation. This is primarily due to game sparsity, where the mere hundreds of games fall short for large-scale user representation learning, and game imbalance, where user behaviors are overwhelmingly dominated by a handful of popular games. To address the sparsity issue, we introduce the User Game Lifecycle (UGL), designed to enrich user behaviors in games. Additionally, we propose two innovative strategies aimed at manipulating user behaviors to more effectively extract both short and long-term interests. To tackle the game imbalance challenge, we present an Inverse Probability Masking strategy for UGL representation learning. The offline and online experimental results demonstrate that the UGL representations significantly enhance model by achieving a 1.83% AUC offline increase on average and a 21.67% CVR online increase on average for game advertising and a 0.5% AUC offline increase and a 0.82% ARPU online increase for in-game item recommendation.
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