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.
Fields of Study & Subjects
Topic Modeling
Artificial Intelligence in Healthcare and Education
Explainable Artificial Intelligence (XAI)
Publication details
- Journal / source
- Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Pages
- 41–52
- Publication date
- 10 July 2026