为新商品冷启动设计智能流量分配系统，提升曝光效率。
Item Level Exploration Traffic Allocation in Large-scale Recommendation Systems
- 用学习的可发现性模型预测新商品曝光潜力。
- 动态分配探索流量，显著提升新内容可见度。
- 适合大规模推荐系统优化冷启动问题的工程师。
本文针对大规模推荐系统中的物品冷启动问题，研究如何高效为新引入内容获取初始曝光。提出一种探索系统，用于高效分配这些新物品的展示机会。方法基于学习的概率模型预测物品的可发现性，并据此制定可扩展且自适应的流量分配策略。该系统智能分配探索预算，以优化推荐平台的长期收益。实际部署证明，该方案显著提升了新内容的可发现性，丰富了可用于推荐的物品库，验证了其在大规模生产环境中的有效性。
原文摘要 · Abstract (English)
This paper contributes to addressing the item cold start problem in large-scale recommender systems, focusing on how to efficiently gain initial visibility for newly ingested content. We propose an exploration system designed to efficiently allocate impressions to these fresh items. Our approach leverages a learned probabilistic model to predict an item's discoverability, which then informs a scalable and adaptive traffic allocation strategy. This system intelligently distributes exploration budgets, optimizing for the long-term benefit of the recommendation platform. The impact is a demonstrably more efficient cold-start process, leading to a significant increase in the discoverability of new content and ultimately enriching the item corpus available for exploitation, as evidenced by its successful deployment in a large-scale production environment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。