In recommender systems, large language models (LLMs) have gained popularity for generating descriptive summarization to improve recommendation robustness, along with Graph Convolution Networks. However, existing LLM-enhanced recommendation studies mainly rely on the internal knowledge of LLMs about item titles while neglecting the importance of various factors influencing users' decisions. Although information reflecting various decision factors of each user is abundant in reviews, few studies have actively exploited such insights for recommendation. To address these limitations, we propose a ReFORM: Review-aggregated Profile Generation via LLM with Multi-FactOr Attentive RecoMmendation framework. Specifically, we first generate factor-specific user and item profiles from reviews using LLM to capture a user's preference by items and an item's evaluation by users. Then, we propose a Multi-Factor Attention to highlight the most influential factors in each user's decision-making process. In this paper, we conduct experiments on two restaurant datasets of varying scales, demonstrating its robustness and superior performance over state-of-the-art baselines. Furthermore, in-depth analyses validate the effectiveness of the proposed modules and provide insights into the sources of personalization. Our source code and datasets are available at https://github.com/m0onsoo/ReFORM.

翻译：在推荐系统中，大语言模型（LLMs）与图卷积网络相结合，通过生成描述性摘要来提高推荐的鲁棒性，已日益受到欢迎。然而，现有基于LLM增强的推荐研究主要依赖于LLM对物品标题的内部知识，而忽略了影响用户决策的各种因素的重要性。尽管反映每位用户多种决策因素的信息在评论中十分丰富，但少有研究积极利用这些洞见进行推荐。为应对这些局限，我们提出了ReFORM：一种基于大语言模型与多因子注意力机制的评论聚合画像生成推荐框架。具体而言，我们首先利用LLM从评论中生成针对特定因子的用户画像和物品画像，以捕捉用户对物品的偏好以及用户对物品的评价。接着，我们提出了一种多因子注意力机制，以突出每个用户决策过程中最具影响力的因子。本文在两个不同规模的餐厅数据集上进行了实验，结果表明该方法具有鲁棒性，且性能优于现有最先进的基线模型。此外，深入分析验证了所提出模块的有效性，并为个性化来源提供了洞见。我们的源代码和数据集可在 https://github.com/m0onsoo/ReFORM 获取。