Beyond the Single Path: Divergent Reasoning for LLM-based Recommendation

Abstract

Large Language Models (LLMs) have demonstrated strong potential in recommendations due to their powerful reasoning capabilities. However, existing methods typically rely on a single reasoning path to drive the entire Top-K recommendations. This paradigm is prone to reasoning path collapse, where limiting exploration of potentially superior and diverse reasoning paths within the LLMs space. As a result, both the accuracy and diversity of the recommendation outcomes are constrained. To address this issue, we propose a novel model, Divergent Reasoning for LLM-based Recommendation, named DivReason. Inspired by the structure of intellect theory, which emphasizes a two-stage cognitive process of divergent thinking followed by convergent thinking, DivReason is designed with two core components: the Divergent Reasoning Path Generation Module and the Reasoning Path Aggregation Module. In the first module, DivReason introduces a training-free form of controlled uncertainty to promote diverse reasoning, leveraging Monte Carlo Dropout and Directional Perturbation to expand exploration in the latent reasoning space. In the Reasoning Path Aggregation Module, we adaptively select a subset of high-quality reasoning paths from the entire path pool and aggregate them into a unified reasoning representation. Meanwhile, we further adopt an alternating reinforcement learning strategy to optimize the model, explicitly balancing accuracy and diversity during training. Extensive experimental results show that DivReason effectively mitigates the issue of reasoning path collapse, while improving both the accuracy and diversity of LLM-based recommendations.