TRACER：平衡大语言模型增强型持续推荐的稳定性-可塑性-认知性三难困境
TRACER: Balancing Stability-Plasticity-Cognitivity Trilemma for LLM Enhanced Continual Recommendation
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中文总结 AI 辅助
针对LLM增强持续推荐的SPC三难困境，提出TRACER模型，通过协同设计三个专用模块协调三难，在五组真实数据集上较最优基线最高提升14.38%
中文摘要 AI 辅助
持续推荐旨在从流式数据中捕捉用户不断变化的兴趣，但面临数据稀疏性问题。大语言模型（LLM）增强器可通过语义知识缓解该问题，但简单集成会产生新冲突，我们将其定义为稳定性-可塑性-认知性（Stability-Plasticity-Cognitivity，SPC）三难困境：通用LLM语义先验（认知性）与保留用户个性化历史偏好（稳定性）、适配个体兴趣变化（可塑性）相冲突。为解决该问题，我们提出用于推荐的三难困境响应式自适应持续增强模型TRACER，其协同融合三个分别针对稳定性、可塑性或认知性的专用模块，同时避免任一维度占主导地位。该整体设计使语义知识能支持历史偏好保留与兴趣变化适配，且不干扰持续学习。在五个真实世界数据集上，TRACER有效协调了SPC三难困境，较当前最优基线模型性能提升最高达14.38%。我们的代码可在该https URL获取。
英文摘要
Continual recommendation aims to capture evolving user interests from streaming data but struggles with sparsity. LLM enhancers mitigate this with semantic knowledge, but naive integration creates a new conflict. We identify this as the Stability-Plasticity-Cognitivity (SPC) Trilemma, where generalized LLM semantic priors (Cognitivity) conflict with retaining personalized historical preferences (Stability) and adapting to individual interest shifts (Plasticity). To address this, we propose Trilemma-Responsive Adaptive Continual Enhancement for Recommendation (TRACER). TRACER synergistically combines three specialized modules, each targeting stability, plasticity, or cognitivity, while preventing any single lemma from dominating. This holistic design enables semantic knowledge to support history retention and adaptation to evolving interests without disrupting continual learning. Across five real-world datasets, TRACER effectively harmonizes the SPC trilemma and outperforms state-of-the-art baselines by up to 14.38%. Our code is available at https://github.com/woo-joo/TRACER_CIKM26.
