Unlike short-video content, music tracks have long lifecycles and lasting value. Effective music search re-ranking must therefore align the user's current query with long-term preferences while jointly optimizing Click-Through Rate (CTR) and Conversion Rate (CVR). However, existing methods suffer from two limitations: (1) sequential methods rely on item-interaction history and therefore cannot use historical search queries to tell which past preferences match the user's current search intent; (2) most listwise models optimize a single objective (e.g., CTR only), and conventional multi-objective methods balance click and conversion at the item level, ignoring how these trade-offs play out across the whole ranked list. To address these limitations, we propose PIANO, a personalized listwise re-ranking framework with two key components: (i) the Query-Driven Interest Refiner (QDIR) uses cross-attention over historical queries to align past intents with the current one; (ii) the Information Aggregation Node (IAN), a learnable [CLS]-style token, aggregates the candidate list and predicts CTR/CVR at the list level. Extensive experiments on public and industrial datasets show consistent gains over strong baselines. In online A/B tests on NetEase Cloud Music, a leading music streaming platform, PIANO achieves statistically significant improvements in CTR (+0.62%) and CVR (+4.45%).

翻译：与短视频内容不同，音乐曲目具有长生命周期和持久价值。有效的音乐搜索重排序必须将用户当前查询与长期偏好对齐，同时联合优化点击率（CTR）和转化率（CVR）。然而，现有方法存在两个局限性：（1）序列方法依赖物品交互历史，因此无法利用历史搜索查询判断哪些历史偏好与用户当前搜索意图相匹配；（2）大多数列表级模型优化单一目标（例如仅优化CTR），而传统多目标方法在物品层面平衡点击与转化，忽略了这些权衡如何在整个排序列表中发挥作用。为解决上述局限性，我们提出PIANO——一种包含两个关键组件的个性化列表级重排序框架：（i）查询驱动兴趣精炼器（QDIR）通过历史查询的交叉注意力机制将过去意图与当前意图对齐；（ii）信息聚合节点（IAN）作为可学习的[CLS]风格标记，聚合候选列表并在列表级别预测CTR/CVR。在公开和工业数据集上的大量实验表明，该方法相较于强基线模型取得了持续提升。在领先音乐流媒体平台网易云音乐的在线A/B测试中，PIANO在CTR（+0.62%）和CVR（+4.45%）上均实现统计显著性提升。