发表机构
The Hong Kong University of Science and Technology; Swinburne University of Technology（香港科技大学; 斯威本科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对MOBA游戏匹配的冷启动、分布不一致等瓶颈，提出跨域混合架构CHAMP，结合DAWN网络实现胜率预测，可降低不平衡对局率，尤其能将低段位玩家5分钟击杀碾压率最多降低20.73%。
AI 中文摘要
多人在线战术竞技（MOBA）游戏依赖匹配机制维持竞技平衡。我们此前的工作CUPID将匹配问题建模为分配重优化问题，结果表明单一模式的胜率预测器可有效重新平衡队伍。但将此类系统部署到不同玩家群体时，会面临三个实际瓶颈：多数排队玩家缺乏足够的目标模式匹配历史（冷启动问题）；不同段位的技能分布存在显著差异（分布不一致问题）；极端技能段的数据严重匮乏。我们提出CHAMP，一种跨域匹配框架，用于解决这些部署瓶颈。为应对数据稀疏性与冷启动问题，CHAMP将仅针对目标模式的玩家档案替换为混合域特征集合：按时间戳排序的跨模式短期序列，其片段标注有目标域特征，以及长期、实时和团队统计数据的分模式分解。我们进一步提出域感知胜率网络（DAWN）：域感知知识提取器（DAKE）将目标模式属性编译为可学习表示，输入域感知时间/空间/排列全网络编码器（DATOE/DASOE/DAPOE），从而在单个共享网络中联合学习模式条件表示与分模式去偏。在线阶段，一个训练好的DAWN即可服务所有支持的模式，仅需分模式位置满意度阈值作为唯一的模式特定参数。离线阶段，DAWN实现了67.73%的胜率预测准确率，优于所有评估的注意力和序列基线模型。在某大型MOBA游戏的全段位天梯中，从新手玩家到精英模式服务的顶级专家玩家的在线A/B测试显示，不平衡对局率持续下降；对于低段位玩家，CHAMP将5分钟击杀碾压率降低了多达20.73%。
英文摘要
Multiplayer Online Battle Arena (MOBA) games rely on matchmaking to maintain competitive balance. Our prior work, CUPID, framed matchmaking as an assignment re-optimization problem and showed that a single-mode win-rate predictor can meaningfully rebalance teams. However, deploying such a system across diverse player populations exposes three practical bottlenecks: most queueing players lack sufficient in-mode match history (cold start), skill distributions shift drastically across rank tiers (distribution inconsistency), and extreme skill segments are severely data-starved. We present CHAMP, a cross-domain matchmaking framework that resolves these deployment bottlenecks. To address data sparsity and cold starts, CHAMP replaces the target-mode-only player profile with a hybrid domain feature collection: a timestamp-ordered cross-mode short-term sequence whose slices are annotated with target-domain features, plus per-mode breakdowns of long-term, real-time and team statistics. We further propose the Domain-Aware Win-rate Network (DAWN): a Domain-aware Knowledge Extractor (DAKE) compiles target-mode attributes into learnable representations that feed Domain-Aware Temporal/Spatial/Permutation OmniNet Encoders (DATOE/DASOE/DAPOE), so that mode-conditioned representations and per-mode debiasing are learned jointly inside a single shared network. Online, one trained DAWN serves every supported mode, with per-mode position-satisfaction thresholds as the only mode-specific knob. Offline, DAWN achieves 67.73% win-rate prediction accuracy, outperforming all evaluated attention and sequence baselines. Online A/B tests across the entire League ladder of a large-scale MOBA game, from novice players up to the top-expert players served by Elite Mode, demonstrate consistent drops in imbalanced matches. For lower-tier players, CHAMP reduces the 5-minute kill crushing rate by up to 20.73%.
CommentsAccepted by CIKM 2026 (Applied Research Track)
