用语义ID融合增强冷启动商品表示，提升电商搜索点击率
COINS: SemantiC Ids Enhanced COLd Item RepresentatioN for Click-through Rate Prediction in E-commerce Search
- 通过语义ID融合内容与协同信息，实现跨商品信号传递
- 在线实验显示点击率提升1.66%，订单量增长2.17%
- 适合关注冷启动问题与推荐系统多样性的工程师
随着现代搜索与推荐平台的发展，冷启动商品的协同信息不足加剧了平台中高热度商品的马太效应，影响平台多样性，成为长期难题。现有方法尝试将商品侧信息与协同信号对齐，以将高热度商品的协同信号迁移至冷启动商品。然而，这些方法未考虑协同与内容之间的不对称性，也忽略了商品间的细粒度差异。为此，我们提出COINS，一种基于语义ID融合对齐的物品表征增强方法。具体而言，采用RQ-OPQ编码对商品内容与协同信息进行量化，再通过两步对齐：RQ编码在商品间传递共享的协同信号，而OPQ编码学习商品的差异化特征。大规模工业数据集上的离线实验验证了COINS的优越性，严格的线上A/B测试表明其效果显著：商品点击率提升1.66%，买家数增加1.57%，订单量增长2.17%。
原文摘要 · Abstract (English)
With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becoming a longstanding issue. Existing methods align items' side content with collaborative information to transfer collaborative signals from high-popularity items to cold-start items. However, these methods fail to account for the asymmetry between collaboration and content, nor the fine-grained differences among items. To address these issues, we propose COINS, an item representation enhancement approach based on fused alignment of semantic IDs. Specifically, we use RQ-OPQ encoding to quantize item content and collaborative information, followed by a two-step alignment: RQ encoding transfers shared collaborative signals across items, while OPQ encoding learns differentiated information of items. Comprehensive offline experiments on large-scale industrial datasets demonstrate superiority of COINS, and rigorous online A/B tests confirm statistically significant improvements: item CTR +1.66%, buyers +1.57%, and order volume +2.17%.
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