SkinAdvisor：用多模态 Transformer 预测皮肤销量的游戏生产决策框架
SkinAdvisor: An AI-Driven Decision Framework for Skin Production Planning via Multi-Modal Transformer Sales Forecasting
游戏行业皮肤收入靠拍脑袋定价的日子可以改改了。这篇论文难得在于系统已真实上线一个完整发布周期，预测精度、CPU 延迟和吞吐数据都给全了，还坦诚讲了预训练嵌入泄露时间信息这种踩坑细节。做销量预测或游戏运营的工程师能直接借鉴架构和两条教训。
SkinAdvisor 是一套面向游戏皮肤的 AI 生产决策框架，核心是已上线部署的 SkinCast 预测模型，可为未发布的皮肤预测 180 天逐日销量轨迹。SkinCast 融合四个编码器：预训练 Informer 处理玩家偏好时间序列、预训练 Player-Hero-Skin 图嵌入提供关系先验，另有两个轻量 Transformer 分别编码皮肤属性和发布上下文，通过共享 backbone 结合局部加全局的 day-x 位置编码，最终由 N 层 Informer 以单次非自回归方式解码输出。
离线实验显示，SkinCast 相比最强基线 Informer 将 RMSE 降低约 20%，相比传统方法降低约 60%。部署侧在 16 核 CPU 服务器上压测，p99 延迟低于 8ms，峰值吞吐超过 1100 rps。SkinAdvisor 将预测结果接入 Select-Fill-Infer-Sales-ROI-Ranking 服务流水线，按预测 ROI 对候选皮肤排序并支持多候选对比，已完整运行一个发布周期。
论文还给出两条部署经验：生产决策只需量级级别的精度即可，预训练嵌入可能泄露时间信息，需要按季节切分数据集防止。
SkinAdvisor: An AI-Driven Decision Framework for Skin Production Planning via Multi-Modal Transformer Sales Forecasting
Skins, cosmetic in-game items, are a primary revenue stream for modern online games, yet production decisions (which skins to make, when to release, how to price) remain driven by intuition and retrospective analytics. We present SkinAdvisor, an AI-driven decision framework for skin production planning, powered by SkinCast, a deployed multi-modal Transformer forecasting 180-day per-day sales trajectories for unreleased skins. SkinCast couples four encoders: a pretrained Informer for player-preference time series, a pretrained Player-Hero-Skin graph embedder for relational priors, and two lightweight Transformers for skin attributes and release context, fused through a shared backbone with a local-plus-global day-x positional encoding and decoded by an N-layer Informer in a single non-autoregressive pass. SkinAdvisor plugs these forecasts into a serving pipeline (Select -> Fill -> Infer -> Sales -> ROI -> Ranking) that ranks candidate skins by forecast-based ROI and supports multi-candidate comparison. The system has been in production for a full release cycle. Offline experiments show SkinCast reduces RMSE by ~20% over the strongest baseline (Informer) and ~60% over classical methods; stress tests on a 16-core CPU server confirm p99 latency under 8ms and peak throughput above 1100 rps. We report two deployment lessons: order-of-magnitude accuracy suffices for production decisions, and pretrained embeddings can leak temporal information, requiring season-based splitting to prevent.
This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SkinAdvisor, SkinCast, Informer, 多模态.
Source attribution: curated from arXiv cs.LG (@Ge Fan, Kai Wang, Chaoyun Zhang, Yingjie Li, Yuze Liu, Junyang Chen).