LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising
Dynamic Product Advertising (DPA) retrieval systems are a core component of modern e-commerce advertising; however, their evaluation remains challenging. Conventional offline engagement metrics are limited by sparse, biased feedback, while online evaluation is costly and time-consuming. In this work, we propose a user--product relevance metric for offline evaluation of DPA retrieval systems. The metric first compresses the engagement history into a compact set of intent representatives via Semantic-ID-grouped, event-stratified segmentation, then assesses the relevance of a recommended product to each individual intent and aggregates them as a user-level relevance score, yielding a stable ...
| Authors | Wen Zhang, Xiao Bai, Hsiang-wei Chao et al. |
|---|---|
| Primary category | cs.IR |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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