Recommendation systems aim to learn user interests from historical behaviors and deliver relevant items. Recent methods leverage large language models (LLMs) to construct and integrate semantic representations of users and items for capturing user interests. However, user behavior theories suggest that truly understanding user interests requires not only semantic integration but also semantic reasoning from explicit individual interests to implicit group interests. To this end, we propose an Iterative Semantic Reasoning Framework (ISRF) for generative recommendation. ISRF leverages LLMs to bridge explicit individual interests and implicit group interests in three steps. First, we perform multi-step bidirectional reasoning over item attributes to infer semantic item features and build a semantic interaction graph capturing users' explicit interests. Second, we generate semantic user features based on the semantic item features and construct a similarity-based user graph to infer the implicit interests of similar user groups. Third, we adopt an iterative batch optimization strategy, where individual explicit interests directly guide the refinement of group implicit interests, while group implicit interests indirectly enhance individual modeling. This iterative process ensures consistent and progressive interest reasoning, enabling more accurate and comprehensive user interest learning. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that ISRF outperforms state-of-the-art baselines. The code is available at https://github.com/htired/ISRF.

翻译：推荐系统旨在从用户历史行为中学习其兴趣偏好，并推送相关物品。现有方法多利用大语言模型构建并整合用户与物品的语义表征以捕捉用户兴趣。然而，用户行为理论指出，真正理解用户兴趣不仅需要语义整合，更需实现从显性个体兴趣到隐性群体兴趣的语义推理。为此，我们提出一种面向生成式推荐的迭代语义推理框架。该框架借助大语言模型，通过三个步骤桥接显性个体兴趣与隐性群体兴趣：首先，通过对物品属性进行多步双向推理，推断语义化物品特征，并构建捕获用户显性兴趣的语义交互图；其次，基于语义化物品特征生成语义化用户特征，构建基于相似度的用户图以推断相似用户群体的隐性兴趣；最后，采用迭代批量优化策略，使个体显性兴趣直接指导群体隐性兴趣的优化，同时群体隐性兴趣间接增强个体建模。这一迭代过程确保了兴趣推理的一致性与渐进性，从而实现更精准、更全面的用户兴趣学习。在Sports、Beauty和Toys数据集上的大量实验表明，本框架性能优于现有先进基线方法。代码已开源：https://github.com/htired/ISRF。