Recent advances in large language models (LLMs) have enabled more semantic-aware recommendations through natural language generation. Existing LLM for recommendation (LLM4Rec) methods mostly operate in a System 1-like manner, relying on superficial features to match similar items based on click history, rather than reasoning through deeper behavioral logic. This often leads to superficial and erroneous recommendations. Motivated by this, we propose ThinkRec, a thinking-based framework that shifts LLM4Rec from System 1 to System 2 (rational system). Technically, ThinkRec introduces a thinking activation mechanism that augments item metadata with keyword summarization and injects synthetic reasoning traces, guiding the model to form interpretable reasoning chains that consist of analyzing interaction histories, identifying user preferences, and making decisions based on target items. On top of this, we propose an instance-wise expert fusion mechanism to reduce the reasoning difficulty. By dynamically assigning weights to expert models based on users' latent features, ThinkRec adapts its reasoning path to individual users, thereby enhancing precision and personalization. Extensive experiments on real-world datasets demonstrate that ThinkRec significantly improves the accuracy and interpretability of recommendations. Our implementations are available at https://github.com/Yu-Qi-hang/ThinkRec.

翻译：近年来，大语言模型（LLMs）的进展使得通过自然语言生成实现更具语义感知的推荐成为可能。现有的大语言模型推荐方法大多以类似系统1的方式运作，依赖表层特征根据点击历史匹配相似物品，而非通过更深层的行为逻辑进行推理，这常常导致推荐结果肤浅且存在错误。受此启发，我们提出了ThinkRec，一个基于思维推理的框架，旨在将大语言模型推荐从系统1转向系统2（理性系统）。在技术上，ThinkRec引入了一种思维激活机制，通过关键词摘要增强物品元数据，并注入合成的推理轨迹，引导模型形成可解释的推理链，包括分析交互历史、识别用户偏好以及基于目标物品做出决策。在此基础上，我们提出了一种实例级专家融合机制以降低推理难度。通过根据用户的潜在特征动态分配专家模型的权重，ThinkRec使其推理路径适应于个体用户，从而提升推荐的准确性和个性化程度。在真实世界数据集上的大量实验表明，ThinkRec显著提高了推荐的准确性和可解释性。我们的实现代码可在 https://github.com/Yu-Qi-hang/ThinkRec 获取。