用替代品行为数据帮新品突破冷启动困局
Behavioral Feature Boosting via Substitute Relationships for E-commerce Search
- 通过识别可替代商品，聚合其点击、购买等行为数据
- 冷启动商品搜索相关性提升，曝光量显著增加
- 适合电商推荐系统优化，尤其对新品推广有效
在电商平台中，新商品常面临冷启动问题：互动数据不足导致搜索可见性低，影响排序相关性。为此，我们提出一种简单有效的行为特征增强方法（BFS），利用商品间的替代关系——即满足相似用户需求的商品——聚合其行为信号（如点击、加购、购买和评分），为新商品提供“热启动”支持。将这些增强后的信号引入排序模型，可缓解冷启动问题，提升相关性和竞争力。在大型电商平台的离线与在线实验均表明，BFS显著改善了冷启动商品的搜索相关性与发现率。该方法具备良好可扩展性与实用性，已部署于生产环境并自2025年起服务用户。
原文摘要 · Abstract (English)
On E-commerce platforms, new products often suffer from the cold-start problem: limited interaction data reduces their search visibility and hurts relevance ranking. To address this, we propose a simple yet effective behavior feature boosting method that leverages substitute relationships among products (BFS). BFS identifies substitutes-products that satisfy similar user needs-and aggregates their behavioral signals (e.g., clicks, add-to-carts, purchases, and ratings) to provide a warm start for new items. Incorporating these enriched signals into ranking models mitigates cold-start effects and improves relevance and competitiveness. Experiments on a large E-commerce platform, both offline and online, show that BFS significantly improves search relevance and product discovery for cold-start products. BFS is scalable and practical, improving user experience while increasing exposure for newly launched items in E-commerce search. The BFS-enhanced ranking model has been launched in production and has served customers since 2025.
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