In real-world applications, users express different behaviors when they interact with different items, including implicit click/like interactions, and explicit comments/reviews interactions. Nevertheless, almost all recommender works are focused on how to describe user preferences by the implicit click/like interactions, to find the synergy of people. For the content-based explicit comments/reviews interactions, some works attempt to utilize them to mine the semantic knowledge to enhance recommender models. However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains? (2) The user/item ID feature is a fundamental element for recommender models; how do we align the ID and content semantic feature space? In this paper, we propose a `plugin' semantic knowledge transferring method \textbf{LoID}, which includes two major components: (1) LoRA-based large language model pretraining to extract multi-aspect semantic information; (2) ID-based contrastive objective to align their feature spaces. We conduct extensive experiments with SOTA baselines on real-world datasets, the detailed results demonstrating significant improvements of our method LoID.

翻译：在现实应用场景中，用户与不同物品交互时表现出差异化行为，包括隐式点击/点赞交互与显式评论/评价交互。然而，几乎所有推荐系统研究工作均聚焦于通过隐式点击/点赞交互描述用户偏好以挖掘人群协同效应。针对基于内容的显式评论/评价交互，部分研究尝试利用其挖掘语义知识以增强推荐模型，但仍忽视以下两点：(1) 内容语义作为普适性世界知识，如何提取多维度语义信息赋能不同领域？(2) 用户/物品ID特征作为推荐模型基础要素，如何实现ID与内容语义特征空间的对齐？本文提出一种即插即用型语义知识迁移方法 \textbf{LoID}，包含两大核心模块：(1) 基于LoRA的大型语言模型预训练以提取多维度语义信息；(2) 基于ID的对比学习目标以实现特征空间对齐。我们在真实数据集上基于当前最优基线模型开展广泛实验，详细结果表明LoID方法具有显著性能提升。