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.

翻译：在电商平台上，新产品常面临冷启动问题：有限的交互数据降低了其搜索可见性并损害相关性排序。为解决此问题，我们提出了一种简单而有效的行为特征增强方法，该方法利用商品间的替代关系（BFS）。BFS识别替代品——即满足相似用户需求的商品——并聚合其行为信号（如点击、加购、购买和评分），为新商品提供热启动。将这些增强信号纳入排序模型可缓解冷启动效应，提升相关性和竞争力。在大型电商平台进行的离线和在线实验表明，BFS显著改善了冷启动商品的搜索相关性和商品发现效果。BFS具备可扩展性和实用性，在提升用户体验的同时增加了新上市商品在电商搜索中的曝光度。经BFS增强的排序模型已于2025年上线生产环境并持续为用户提供服务。