发表机构
Amazon.com Inc.（亚马逊公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电商产品媒体排序问题，本文提出SMEO框架，通过轨迹效用模型和生存加权自回归策略优化，提升转化率并减少消费者划动次数。
AI 中文摘要
在现代电商平台中，消费者在做出购买决策前会浏览图像、视频、3D渲染等异构产品媒体的有序列表。现有的媒体排序系统通常优化点击量或停留时间等短视参与代理指标，而未意识到产品媒体资产是同一商品的协作信息组件，共同帮助消费者通过序列交互找到所需信息。本文提出面向消费者的媒体排序两阶段效用导向框架——序列多模态证据优化（SMEO）：首先从已消费的媒体前缀中学习轨迹效用模型，估计有序证据如何帮助消费者达成购买决策，同时缓解日志数据中的位置偏差和变量深度不平衡问题；考虑到消费者注意力是有限资源，SMEO随后训练带生存加权回报的自回归排序策略，优先在早期提供最具决策相关性的信息，让消费者以更少的精力找到所需内容。通过将效用学习与策略优化解耦，SMEO可从有偏差的日志中进行稳定的离线学习，且无需明确的媒体级标签即可实现事后媒体归因。使用双重鲁棒离线策略估计在大规模电商会话上进行离线评估，结果显示SMEO的估计转化率提升5.5%，且帮助消费者达成购买决策所需的划动次数比现有基准少15%。
英文摘要
On modern e-commerce stores, customers consume ordered slates of heterogeneous product media, such as images, videos, and 3D renders, before making purchase decisions. Existing media-ranking systems often optimize myopic engagement proxies such as clicks or dwell time, even though product media assets are cooperative informational components of the same item that together help customers find the information they need through sequential interaction. We present Sequential Multimodal Evidence Optimization (SMEO), a two-stage utility-guided framework for customer-oriented media sequencing. SMEO first learns a trajectory utility model from consumed media prefixes to estimate how ordered evidence helps customers reach a purchase decision, while mitigating position-bias and variable-depth imbalance in logged data. Recognizing that customer attention is a limited resource, it then trains an autoregressive ranking policy with survival-weighted reward-to-go that prioritizes the most decision-relevant information early, so customers can find what they need with less effort. By decoupling utility learning from policy optimization, SMEO enables stable offline learning from biased logs and post-hoc media attribution without explicit media-level labels. Evaluated offline on large-scale e-commerce sessions using doubly robust off-policy estimation, SMEO improves estimated conversion by 5.5% and helps customers reach a purchase decision with 15% fewer swipes than existing baselines.
CommentsProceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026), Rome, Italy
