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.

Unsupervised cross-domain dependency parsing is to accomplish domain adaptation for dependency parsing without using labeled data in target domain. Existing methods are often of the pseudo-annotation type, which generates data through self-annotation of the base model and performing iterative training. However, these methods fail to consider the change of model structure for domain adaptation. In addition, the structural information contained in the text cannot be fully exploited. To remedy these drawbacks, we propose a Semantics-Structure Adaptative Dependency Parser (SSADP), which accomplishes unsupervised cross-domain dependency parsing without relying on pseudo-annotation or data selection. In particular, we design two feature extractors to extract semantic and structural features respectively. For each type of features, a corresponding feature adaptation method is utilized to achieve domain adaptation to align the domain distribution, which effectively enhances the unsupervised cross-domain transfer capability of the model. We validate the effectiveness of our model by conducting experiments on the CODT1 and CTB9 respectively, and the results demonstrate that our model can achieve consistent performance improvement. Besides, we verify the structure transfer ability of the proposed model by introducing Weisfeiler-Lehman Test.