Eliciting Interaction-of-Thought Reasoning in LLM Agents for Personalized Recommendation

The emergent reasoning capabilities in Large Language Models (LLMs) have given rise to a surge of research interests in developing personalized agentic systems, particularly in the field of information retrieval. However, it remains a challenge to harness the emergent reasoning capabilities of LLMs for personalized recommendation tasks, largely because general natural language corpora inherently lack recommendation-oriented reasoning traces. To bridge this gap, we introduce R2Rec, a reasoning-enhanced recommendation framework that derives recommendation-oriented reasoning supervision directly from user-item interactions. R2Rec samples interaction chains from the user-item graph to expose high-order collaborative signals and transforms them into structured Interaction-of-Thought traces through a progressive, masked agentic pipeline. By grounding intermediate reasoning in progressively revealed interaction evidence while keeping the final conclusion consistent with the user’s actual interaction, these traces provide step-wise supervision for preference reasoning. We then internalize and further refine this capability through post-training: synthesized traces are used for supervised learning of structured reasoning over interaction chains, while recommendation-specific rewards encourage sufficient reasoning decomposition and accurate candidate ranking. Experimental results on three real-world datasets show that R2Rec improves over its backbone LLM by 195.12% on average across three ranking metrics and outperforms state-of-the-art baselines by 9.87%. Furthermore, a human study demonstrates the superior interpretability of R2Rec's reasoning, which achieves a 9.07% improvement over existing methods. Our code is open-source at https://anonymous.4open.science/r/R2Rec-7C5D for reproducibility.