Selective LLM-Guided Regularization for Enhancing Recommendation Models

Large language models (LLMs) provide rich semantic priors and strong reasoning capabilities, making them promising auxiliary signals for recommendation. However, prevailing approaches either deploy LLMs as standalone recommenders or apply global knowledge distillation, both of which suffer from inherent drawbacks. Standalone LLM recommenders are costly, biased, and unreliable across large regions of the user-item space, while global distillation forces the downstream model to imitate LLM predictions even when such guidance is inaccurate. Meanwhile, recent studies show that LLMs excel particularly in re-ranking and challenging scenarios, rather than uniformly across all contexts. We introduce Selective LLM-Guided Regularization (S-LLMR), a model-agnostic and computation-efficient framework that activates LLM-based pairwise ranking supervision only when a trainable gating mechanism-informed by user history length, item popularity, and model uncertainty predicts the LLM to be reliable. All LLM scoring is done offline, transferring knowledge without increasing inference cost. Experiments across multiple datasets show that this selective strategy consistently improves overall accuracy and yields substantial gains in cold-start and long-tail regimes, outperforming global distillation baselines.