Large Language Models (LLMs) have become powerful foundations for generative recommender systems, framing recommendation tasks as text generation tasks. However, existing generative recommendation methods often rely on discrete ID-based prompts or task-specific soft prompts, which overlook the valuable collaborative signals shared among users with similar interests. To address this limitation, this paper presents a compositional framework that integrates a user's individual preferences with collective preferences from similar users to build personalized soft prompts. Specifically, an attention-based mechanism fuses embeddings from users with similar interests, creating a richer representation that captures multiple facets of user preferences. This design dynamically emphasizes shared interests while preserving individual user preferences. Experiments on three real-world datasets demonstrate the effectiveness of the proposed approach across sequential recommendation, top-n recommendation, and explanation generation tasks, underscoring the advantages of incorporating collaborative signals through an attention-based compositional strategy.

翻译：大型语言模型（LLMs）已成为生成式推荐系统的强大基础，将推荐任务转化为文本生成任务。然而，现有生成式推荐方法通常依赖离散的基于ID的提示或任务特定的软提示，忽视了具有相似兴趣的用户之间共享的宝贵协作信号。为解决这一局限，本文提出一种组合式框架，将用户的个体偏好与来自相似用户的集体偏好相整合，以构建个性化软提示。具体而言，基于注意力机制的模块融合了具有相似兴趣的用户嵌入，创建了更丰富的表征来捕捉用户偏好的多面性。该设计在动态强调共享兴趣的同时，保留了个体用户的偏好。在三个真实数据集上的实验证明了所提方法在序列推荐、top-n推荐及解释生成任务中的有效性，突出了通过基于注意力机制的组合策略纳入协作信号的优势。