Textual user profiling is an effective paradigm for LLM-based recommendation, but existing methods usually treat profile generation as a one-shot summarization problem. This limitation becomes more severe in long-history recommendation, where early preference signals may be overcompressed and recent behaviors may be overemphasized. We propose LUPE, a Long-history User Profile Evolution framework for LLM-based recommendation. Instead of generating a static profile from the full history, LUPE maintains a persistent textual profile and updates it progressively over temporally ordered interaction chunks. This allows the model to preserve long-term preferences while incorporating new behavioral evidence. To learn how profiles should evolve, LUPE optimizes a profile evolution policy via reinforcement learning using feedback from a frozen downstream LLM-based recommender. This allows the model to directly align profile updates with recommendation utility rather than relying on heuristic or supervised profile targets. Experiments on Yelp, Amazon CDs and Vinyl, and Amazon Books show that LUPE consistently outperforms strong baselines on both rating prediction and ranking tasks. Further analyses show that explicit profile evolution is especially helpful for users with long and heterogeneous interaction histories. The code repository: https://github.com/LongHistoryProfile/long_history_profile.

In Embedding Based Retrieval (EBR), Approximate Nearest Neighbor (ANN) algorithms are widely adopted for efficient large-scale search. However, recent studies reveal a query out-of-distribution (OOD) issue, where query and base embeddings follow mismatched distributions, significantly degrading ANN performance. In this work, we empirically verify the generality of this phenomenon and provide a quantitative analysis. To mitigate the distributional gap, we introduce a distribution regularizer into the encoder training objective, encouraging alignment between query and base embeddings. Extensive experiments across multiple datasets, encoders, and ANN indices show that our method consistently improves retrieval performance.