PIANO: Personalized Reranking via Information Aggregation Node for Music Search Optimization
2026-06-15 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors present PIANO, a new system to improve music search results by better understanding what a user wants based on their current and past search queries. Unlike previous methods that only look at what items users clicked on, their approach uses attention to connect old search intents with the current one. They also introduce a way to evaluate the whole list of recommended tracks to optimize both how often users click and how often they convert (e.g., play or buy). Tests on real data, including on NetEase Cloud Music, show that PIANO improves both click-through and conversion rates.
music searchre-rankingclick-through rate (CTR)conversion rate (CVR)listwise modelcross-attentionuser intentquery-driven interestA/B testingrecommendation system
Authors
Weisheng Li, Chuqiao Huang, Pengcheng Li, Zhengchao Peng, Qiang Xiao, Zhongqian Xie, Qiang Huang, Chuanjiang Luo
Abstract
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%).