针对流媒体广告多模态推荐难题，提出音频主导的混合专家模型。
An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
- 采用模态感知分组与自适应损失掩码，融合多模态特征。
- 音频广告点击率提升14.5%，视频广告提升1.3%，出价成本降低4.8%。
- 适合大规模音频优先平台的跨模态广告系统优化。
Spotify 是一个拥有超过 6.75 亿月活跃用户的大型多媒体平台，用户每日消费数百万小时的音乐、播客、有声书和视频内容。这种多元内容消费模式为计算广告带来独特挑战，需在单一用户体验中有效整合音频、视频和展示等多种广告形式。传统广告推荐模型主要面向前景化场景设计，难以兼顾平台固有的音频中心特性与多格式广告表现优化需求。为此，我们提出跨模态自适应专家混合（CAMoE）框架，用于优化音频主导及多模态场景下的点击率（CTR）预测。CAMoE 通过引入模态感知任务分组、自适应损失掩码和深度交叉网络（DCN），增强传统混合专家模型，以捕捉多模态广告生态中的复杂特征交互。大量消融实验表明，该方法在音频、视频和展示广告格式上均达到近帕累托最优性能，显著优于传统单任务与基于内容的多任务学习基线。在 Spotify 广告服务平台上的规模化部署显示，CAMoE 使音频广告的点击率提升 14.5%，视频广告提升 1.3%，音频广告的预期每点击成本（eCPC）降低 4.8%。
原文摘要 · Abstract (English)
Spotify, a large-scale multimedia platform, attracts over 675 million monthly active users who collectively consume millions of hours of music, podcasts, audiobooks, and video content. This diverse content consumption pattern introduces unique challenges for computational advertising, which must effectively integrate a variety of ad modalities, including audio, video, and display, within a single user experience. Traditional ad recommendation models, primarily designed for foregrounded experiences, often struggle to reconcile the platform's inherent audio-centrality with the demands of optimizing ad performance across multiple formats and modalities. To overcome these challenges, we introduce Cross-modal Adaptive Mixture-of-Experts (CAMoE), a novel framework for optimizing click-through rate (CTR) prediction in both audio-centric and multi-modal settings. CAMoE enhances traditional mixture-of-experts models by incorporating modality-aware task grouping, adaptive loss masking, and deep-cross networks (DCN) to capture complex feature interactions within a multi-modal ad ecosystem. Through extensive ablation studies, we demonstrate that this approach achieves near Pareto-optimal performance across audio, video, and display ad formats, significantly improving AUC-PR compared to conventional single-task and content-based multi-task learning baselines. When deployed at scale on Spotify's ad serving platform, CAMoE delivered substantial gains, yielding a 14.5% increase in CTR for audio ads, a 1.3% increase for video ads, and a 4.8% reduction in expected cost-per-click (eCPC) for audio slots.
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