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 counterparts, making it difficult to systematically evaluate the impact of personalized expression differences on retrieval outcomes. To this end, we propose PersonalQuery-Benchmark (PQB). PQB models personalized syntactic expression and personalized writing errors from users' historical texts, constructs personalized syntactic expression queries and personalized writing-error queries, and validates benchmark quality through LLM assessment and independent human evaluation. PQB covers three Amazon product domains with 6,516 query instances in total, and is evaluated on five retrieval models representing lexical, learned sparse, dense bi-encoder, late-interaction, and hybrid architectures, together with two LLM-based ranking models. Results show that even when the information need is held fixed, personalized expression differences still induce measurable ranking variation; different retrieval models exhibit clearly different sensitivities to syntactic variation and writing-error variation, while higher overall retrieval effectiveness does not necessarily correspond to stronger robustness against expression variation. PQB provides a new benchmark and evaluation framework for systematically studying personalized expression robustness. The associated code and dataset are available at the following address: https://github.com/cpxjazz123/PersonalQuery-Benchmark.

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