Abstract
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. To tackle the diversity of opinions, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several realworld datasets demonstrate the superiority of our FineRec over existing state-of-the-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.
Abstract (translated)
序列推荐旨在根据用户的浏览历史行为提供感兴趣的物品。用户在物品评论中表达的属性-意见对提供了捕捉用户偏好和物品特征的细粒度可能性。为此，我们提出了FineRec框架，该框架探索了评论中的属性-意见对，以细粒度处理序列推荐。具体来说，我们利用一个大语言模型从评论中提取属性-意见对。对于每个属性，创建一个独特的属性特定用户-意见-物品图，其中相应的意见作为连接异质用户和物品节点的边。为解决不同意见的多样性，我们设计了一个多样性感知卷积操作，用于汇总图中的信息，实现属性特定的用户和物品表示学习。最后，我们提出了一个交互式融合机制，将所有属性的属性特定用户/物品表示集成到生成推荐中。在多个现实世界数据集上进行的实验证实了我们的FineRec框架在现有技术水平上具有优越性。进一步的分析还证实了我们在处理任务上的细粒度方法的有效性。
URL
https://arxiv.org/abs/2404.12975
