PersonalQuery-Benchmark：评估检索对个性化表达差异的鲁棒性
原标题：PersonalQuery-Benchmark:Evaluating Retrieval Robustness to Personalized User Expression Differences
AI 摘要
该论文提出 PersonalQuery-Benchmark（PQB），用于评估检索系统对个性化表达差异的鲁棒性。PQB 基于用户历史文本建模个性化句法表达与个性化书写错误，构建覆盖三个亚马逊商品领域、共 6516 条查询实例的基准，并用 LLM 评估与人工评估验证质量。在五类检索模型和两个 LLM 排序模型上的实验显示，即使信息需求固定，个性化表达差异仍会引起可测量的排序变化，且整体检索效果更强并不必然意味着对表达变化更鲁棒。
正文节选
PersonalQuery-Benchmark: Evaluating Retrieval Robustness to Personalized User Expression Differences Abstract. Existing personalized information retrieval methods mainly model differences in user needs through user preferences and query intent, while paying little attention to the personalized expression differences that arise when different users express the same retrieval need. Existing real query data typically lack stable user identities, long-term expression histories, and intent-matched co