Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs
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 ...
| Authors | Xiaofei Zhu, Jinfei Chen, Feiyang Yuan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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