In real-world applications, users always interact with items in multiple aspects, such as through implicit binary feedback (e.g., clicks, dislikes, long views) and explicit feedback (e.g., comments, reviews). Modern recommendation systems (RecSys) learn user-item collaborative signals from these implicit feedback signals as a large-scale binary data-streaming, subsequently recommending other highly similar items based on users' personalized historical interactions. However, from this collaborative-connection perspective, the RecSys does not focus on the actual content of the items themselves but instead prioritizes higher-probability signals of behavioral co-occurrence among items. Consequently, under this binary learning paradigm, the RecSys struggles to understand why a user likes or dislikes certain items. To alleviate it, some works attempt to utilize the content-based reviews to capture the semantic knowledge to enhance recommender models. However, most of these methods focus on predicting the ratings of reviews, but do not provide a human-understandable explanation.

翻译：在实际应用中，用户通常通过多个维度与物品进行交互，例如通过隐式二元反馈（如点击、不感兴趣、长时观看）和显式反馈（如评论、评分）。现代推荐系统（RecSys）从这些隐式反馈信号中学习用户-物品的协同信号，将其视为大规模二元数据流，随后根据用户的个性化历史交互推荐其他高度相似的物品。然而，从这种协同关联的视角来看，推荐系统并不关注物品本身的实际内容，而是优先考虑物品间行为共现的高概率信号。因此，在这种二元学习范式下，推荐系统难以理解用户为何喜欢或不喜欢某些物品。为了缓解这一问题，部分研究尝试利用基于内容的评论来捕捉语义知识以增强推荐模型。然而，这些方法大多侧重于预测评论的评分，而未能提供人类可理解的解释。