E-commerce sellers are advised to bid on keyphrases to boost their advertising campaigns. These keyphrases must be relevant to prevent irrelevant items from cluttering Search systems and to maintain positive seller perception. It is vital that keyphrase suggestions align with seller, Search, and buyer judgments. Given the challenges in collecting negative feedback in these systems, LLMs have been used as a scalable proxy for human judgments. We present an empirical study on a major e-commerce platform of a distillation framework involving an LLM teacher, a cross-encoder assistant and a bi-encoder Embedding Based Retrieval (EBR) student model, aimed at mitigating click-induced biases and provide more diverse keyphrase recommendations while aligning advertising, search and buyer preferences.

翻译：为提升广告活动效果，电商卖家通常被建议对关键词进行竞价投放。这些关键词必须具有相关性，以避免无关商品充斥搜索系统，并维持卖家正面感知。确保关键词建议与卖家、搜索系统及买家判断相一致至关重要。鉴于此类系统中收集负面反馈存在挑战，大语言模型已被用作人类判断的可扩展代理。本研究在主要电商平台上开展了一项实证研究，提出包含LLM教师模型、交叉编码器辅助模型及基于嵌入检索的双编码器学生模型的蒸馏框架，旨在缓解点击诱导偏差，在协调广告、搜索与买家偏好的同时，提供更多样化的关键词推荐。