LLMs have garnered substantial attention in recommendation systems. Yet they fall short of traditional recommenders when capturing complex preference patterns. Recent works have tried integrating traditional recommendation embeddings into LLMs to resolve this issue, yet a core gap persists between their continuous embedding and discrete semantic spaces. Intuitively, textual attributes derived from interactions can serve as critical preference rationales for LLMs' recommendation logic. However, directly inputting such attribute knowledge presents two core challenges: (1) Deficiency of sparse interactions in reflecting preference hints for unseen items; (2) Substantial noise introduction from treating all attributes as hints. To this end, we propose a preference hint discovery model based on the interaction-integrated knowledge graph, enhancing LLM-based recommendation. It utilizes traditional recommendation principles to selectively extract crucial attributes as hints. Specifically, we design a collaborative preference hint extraction schema, which utilizes semantic knowledge from similar users' explicit interactions as hints for unseen items. Furthermore, we develop an instance-wise dual-attention mechanism to quantify the preference credibility of candidate attributes, identifying hints specific to each unseen item. Using these item- and user-based hints, we adopt a flattened hint organization method to shorten input length and feed the textual hint information to the LLM for commonsense reasoning. Extensive experiments on both pair-wise and list-wise recommendation tasks verify the effectiveness of our proposed framework, indicating an average relative improvement of over 3.02% against baselines.

翻译：大型语言模型在推荐系统中已获得广泛关注，但其在捕捉复杂偏好模式方面仍不及传统推荐方法。近期研究尝试将传统推荐嵌入整合至LLM以解决此问题，然而连续嵌入空间与离散语义空间之间的核心隔阂依然存在。从直觉上看，交互衍生的文本属性可作为LLM推荐逻辑的关键偏好依据。但直接输入此类属性知识面临两大核心挑战：(1)稀疏交互在反映未见项目偏好提示方面存在不足；(2)将所有属性视为提示会引入大量噪声。为此，我们提出基于交互集成知识图谱的偏好提示发现模型，以增强基于LLM的推荐性能。该模型运用传统推荐原理选择性提取关键属性作为提示。具体而言，我们设计了协同偏好提示提取框架，利用相似用户显式交互的语义知识作为未见项目的提示。此外，我们开发了实例级双重注意力机制来量化候选属性的偏好可信度，从而识别针对每个未见项目的特定提示。基于这些项目与用户导向的提示，我们采用扁平化提示组织方法缩短输入长度，并将文本提示信息输入LLM进行常识推理。在成对推荐与列表推荐任务上的大量实验验证了所提框架的有效性，相较于基线模型平均相对提升超过3.02%。