LLM Retrieval for Stable and Predictable Ad Recommendations
Traditional ads recommendation systems have primarily focused on optimizing for prediction accuracy of click or conversion events using canonical metrics such as recall or normalized discounted cumulative gain (NDCG). With the hyper-growth of ads inventory and liquidity with generative AI technologies, the prediction stability and predictability is becoming increasingly critical. Intuitively, prediction stability and predictability can be defined to quantify system robustness with respect to minor/noisy input (ads, creatives) perturbations, the lack of which could lead to advertiser perceivable problems such as repeatability, cold start and under-exploration. In this paper, we introduce a...
| Authors | Vinodh Kumar Sunkara, Satheeshkumar Karuppusamy, Hangjun Xu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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