Large Language Models (LLMs) provide rich semantic representations for recommender systems, but their semantic similarity may not align with collaborative preference patterns captured by collaborative filtering (CF). Directly combining LLM and CF representations can therefore distort the structural relationships encoded in the interaction graph. We propose Structure-Preserving Alignment (SPA), which reformulates CF–LLM integration as language-supervised representation alignment. SPA derives collaborative anchors from frozen CF representations and aligns multiple LLM-generated semantic views toward these anchors through cross-profile view competition. The aligned collaborative representations are then used for structure-preserving two-channel retrieval, which constructs behavior-aware context for downstream LLM-based preference prediction. Experiments on multiple datasets and CF backbones show that SPA consistently improves click-through rate (CTR)-style binary preference prediction.