发表机构
eBay（易趣）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出边际千次展示预期成本（meCPM）框架，将固定价格eCPM扩展至拍卖及ABIN商品，实现多格式列表统一排名，经在线A/B测试验证可提升收益与用户指标并已落地生产。
AI 中文摘要
电子商务搜索排名在将展示位分配给竞争列表时，必须平衡相关性、用户参与度和平台收益等多个目标。对于固定价格商品，预期收益部分的估算已被充分理解，但当平台库存包含混合列表格式（如纯拍卖和混合型“立即购买拍卖（ABIN）”商品）时，估算会变得具有挑战性，这类商品的价格会动态变化，且最终交易价值在排名时尚未知。然而，拍卖和ABIN列表在eBay等平台的库存和交易量中占相当大的比例，也是个体卖家和价值不明确的独特商品的流行格式。我们将标准的千次展示预期成本（eCPM）框架扩展到拍卖和ABIN商品，推导了边际eCPM（meCPM），用于捕捉展示一个价格仍在动态变化的商品所带来的增量价值。所得公式将已固有具备边际性的熟悉固定价格eCPM扩展至拍卖动态场景，允许在单一目标下对固定价格、拍卖和ABIN列表进行统一排名。随后，我们描述了一种实用的生产实现方式，该方式对这一目标进行近似，通过从现有参与度模型中引导来解决冷启动挑战。在某大型电子商务平台进行的在线A/B测试显示，收益实现了正向提升，用户指标也取得了统计上显著的改善，且该系统已部署至生产环境。
英文摘要
E-commerce search ranking must balance multiple objectives--relevance, user engagement, and platform revenue--when allocating impression slots to competing listings. Estimating the expected revenue component is well understood for fixed-price items, but becomes challenging when marketplace inventory includes mixed listing formats such as pure auctions and hybrid "Auction with Buy It Now" (ABIN) items, where prices evolve dynamically and the final transaction value is unknown at ranking time. Yet auction and ABIN listings account for a meaningful share of inventory and transaction volume on platforms such as eBay, and are a popular format for individual sellers and for unique items with unclear value. We extend the standard Expected Cost-per-Mille (eCPM) framework to auction and ABIN listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing one more impression of an item whose price is still evolving. The resulting formulation extends the familiar fixed-price eCPM--which is already inherently marginal--to auction dynamics, allowing unified ranking of fixed-price, auction, and ABIN listings under a single objective. We then describe a practical production implementation that approximates this objective, addressing cold-start challenges by bootstrapping from existing engagement models. Online A/B tests at a large e-commerce platform showed positive revenue gains and statistically significant improvements to user metrics, and the system was deployed to production.
CommentsAccepted at the SIGIR eCom'26 Workshop, July 24, 2026, Melbourne, Australia
