用实时库存优化广告查询，提升点击率和收入
Next-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG) Based GenAI
- 结合实时库存与历史数据生成更相关的广告查询
- 填充值提升68%，相关性指标平衡改善
- 适合电商广告系统优化与生成式AI落地
sponsored search 在电商收入中至关重要，但大量查询无法匹配到广告，造成收入流失。本文提出基于生成式AI的库存感知检索增强生成模型（InvAwr-RAG），融合语义检索与实时库存数据，动态生成与现有商品库存及广告活动匹配的查询。该方法结合历史成功查询与实时数据，多样化重写查询以提升相关性与用户参与度。初步结果显示，广告填充值显著提升68%，相关性指标保持平衡，展现出在沃尔玛数字平台上大幅提升广告收益、广告主回报率及用户体验的潜力。
原文摘要 · Abstract (English)
Sponsored search plays a crucial role in e-commerce revenue generation, where advertisers strategically bid on keywords to capture the attention of users through relevant search queries. However, the process of identifying pertinent keywords for a given query presents significant challenges because of a vast and evolving keyword landscape, ambiguous intentions, and topic diversity. This paper highlights an opportunity for to earn a considerable amount of Ads revenue and user engagement where a significant proportion of queries fail to retrieve any sponsored ads. To utilize this opportunity, we introduce the Inventory-Aware RAG-based Generative AI model (InvAwr-RAG), which integrates advanced semantic retrieval and real-time inventory data. This model combines dynamically generated and historically successful queries to align with available inventory and ad campaigns while diversifying rewritten queries to enhance relevance and user engagement. Preliminary results show a significant 68% increase in fill rate and balanced relevance metrics, indicating a strong potential for increased ad revenue. The InvAwr-RAG model sets a new standard in dynamic query optimization, significantly improving ad relevancy, advertiser ROI, and user experience on Walmart's digital platform.
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