ThinkRec: 基于思考的推荐方法
ThinkRec: Thinking-based recommendation via LLM
AI总结：
ThinkRec通过引入思考激活机制和实例级专家融合机制，提升推荐系统的准确性和可解释性。
摘要：
近年来，大语言模型（LLMs）的进步使得通过自然语言生成实现更语义感知的推荐成为可能。现有的LLM推荐（LLM4Rec）方法大多以系统1的方式运作，依赖于表面特征来根据点击历史匹配相似项目，而不是通过更深层次的行为逻辑进行推理。这往往导致推荐表面化和错误。受此启发，我们提出了ThinkRec，一种基于思考的框架，将LLM4Rec从系统1转向系统2（理性系统）。技术上，ThinkRec引入了一个思考激活机制，通过关键词摘要增强项目元数据，并注入合成推理轨迹，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项目做出决策。此外，我们提出了一种实例级专家融合机制以减少推理难度。通过根据用户的潜在特征动态分配专家模型的权重，ThinkRec使推理路径适应个体用户，从而提高精度和个性化程度。在真实世界数据集上的广泛实验表明，ThinkRec显著提高了推荐的准确性和可解释性。我们的实现可在https://github.com/Yu-Qi-hang/ThinkRec上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
