Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs

ArXi:2603.13934v1 Announce Type: cross 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.