Last year has witnessed the considerable interest of Large Language Models (LLMs) for their potential applications in recommender systems, which may mitigate the persistent issue of data sparsity. Though large efforts have been made for user-item graph augmentation with better graph-based recommendation performance, they may fail to deal with the dynamic graph recommendation task, which involves both structural and temporal graph dynamics with inherent complexity in processing time-evolving data. To bridge this gap, in this paper, we propose a novel framework, called DynLLM, to deal with the dynamic graph recommendation task with LLMs. Specifically, DynLLM harnesses the power of LLMs to generate multi-faceted user profiles based on the rich textual features of historical purchase records, including crowd segments, personal interests, preferred categories, and favored brands, which in turn supplement and enrich the underlying relationships between users and items. Along this line, to fuse the multi-faceted profiles with temporal graph embedding, we engage LLMs to derive corresponding profile embeddings, and further employ a distilled attention mechanism to refine the LLM-generated profile embeddings for alleviating noisy signals, while also assessing and adjusting the relevance of each distilled facet embedding for seamless integration with temporal graph embedding from continuous time dynamic graphs (CTDGs). Extensive experiments on two real e-commerce datasets have validated the superior improvements of DynLLM over a wide range of state-of-the-art baseline methods.

翻译：摘要：过去一年中，大语言模型（LLMs）因其在推荐系统中的潜在应用而受到广泛关注，有望缓解长期存在的数据稀疏性问题。尽管人们已在用户-物品图增强方面做出大量努力以提升基于图的推荐性能，但这些方法难以应对涉及结构与时间双重动态性的动态图推荐任务，处理时间演化数据时存在固有复杂性。为弥补这一空白，本文提出名为DynLLM的新型框架，利用LLMs处理动态图推荐任务。具体而言，DynLLM借助LLMs能力，基于历史购买记录丰富的文本特征生成多维度用户画像，包括人群细分、个人兴趣、偏好品类及青睐品牌，进而补充和丰富用户与物品间的潜在关联。为融合多维度画像与时间图嵌入，我们引入LLMs生成相应画像嵌入，并采用蒸馏注意力机制精炼LLM生成的嵌入以抑制噪声信号，同时评估和调整每个蒸馏维度嵌入的相关性，使其与连续时间动态图（CTDGs）的时间图嵌入无缝集成。在两个真实电商数据集上的大量实验表明，DynLLM相较于多种最先进基线方法展现出显著优势。