Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. This ''rich-get-richer'' phenomenon hinders the growth of potentially valuable cold items and harms the platform's ecosystem. Existing cold-start models primarily focus on improving initial recommendation performance for cold items but fail to address users' natural preference for popular content. In this paper, we introduce AliBoost, Alibaba's ecological boosting framework, designed to complement user-oriented natural recommendations and foster a healthier ecosystem. AliBoost incorporates a tiered boosting structure and boosting principles to ensure high-potential items quickly gain exposure while minimizing disruption to low-potential items. To achieve this, we propose the Stacking Fine-Tuning Cold Predictor to enhance the foundation CTR model's performance on cold items for accurate CTR and potential prediction. AliBoost then employs an Item-oriented Bidding Boosting mechanism to deliver cold items to the most suitable users while balancing boosting speed with user-personalized preferences. Over the past six months, AliBoost has been deployed across Alibaba's mainstream platforms, successfully cold-starting over a billion new items and increasing both clicks and GMV of cold items by over 60% within 180 days. Extensive online analysis and A/B testing demonstrate the effectiveness of AliBoost in addressing ecological challenges, offering new insights into the design of billion-scale recommender systems.

翻译：在十亿级在线平台中维持健康的生态系统具有挑战性，因为用户天然倾向于热门商品，导致冷门及未被充分发掘的商品被忽视。这种“富者愈富”现象阻碍了具有潜在价值的冷门商品成长，并损害平台生态。现有的冷启动模型主要关注提升冷门商品的初始推荐性能，但未能解决用户对热门内容的天然偏好。本文提出AliBoost——阿里巴巴的生态助推框架，旨在补充以用户为中心的自然推荐，并培育更健康的生态系统。AliBoost采用分层助推结构与助推原则，确保高潜力商品快速获得曝光，同时最小化对低潜力商品的干扰。为实现这一目标，我们提出堆叠微调冷门预测器，以增强基础CTR模型在冷门商品上的性能，从而精准预测CTR与商品潜力。随后，AliBoost通过商品导向的竞价助推机制，将冷门商品推送给最合适的用户，并在助推速度与用户个性化偏好之间取得平衡。过去六个月中，AliBoost已在阿里巴巴主流平台全面部署，成功冷启动超十亿新商品，并在180天内使冷门商品的点击量与交易总额均提升60%以上。大量在线分析与A/B测试证明了AliBoost在应对生态挑战方面的有效性，为十亿级推荐系统的设计提供了新思路。