MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedback

2025-10-26

AI Summary

Existing reasoning-based recommendation methods are confined to language-space modeling, often leading to over-interpretation of user interests and misalignment with actual items. To address this, we propose a closed-loop reasoning framework grounded in the item space: (1) a multi-round grounding mechanism dynamically anchors large language model (LLM) reasoning steps to the real item corpus; and (2) a lightweight user-agent feedback module enables real-time calibration and iterative refinement of reasoning paths. This is the first approach enabling controllable, verifiable, closed-loop LLM reasoning directly within the concrete item space. Experiments on three Amazon datasets demonstrate substantial improvements over state-of-the-art reasoning-based recommenders—achieving an average 12.7% gain in Recall@20—validating the critical role of item-space grounding in enhancing both recommendation accuracy and fidelity to user intent.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The powerful reasoning and generative capabilities of large language models (LLMs) have inspired researchers to apply them to reasoning-based recommendation tasks, which require in-depth reasoning about user interests and the generation of recommended items. However, previous reasoning-based recommendation methods have typically performed inference within the language space alone, without incorporating the actual item space. This has led to over-interpreting user interests and deviating from real items. Towards this research gap, we propose performing multiple rounds of grounding during inference to help the LLM better understand the actual item space, which could ensure that its reasoning remains aligned with real items. Furthermore, we introduce a user agent that provides feedback during each grounding step, enabling the LLM to better recognize and adapt to user interests. Comprehensive experiments conducted on three Amazon review datasets demonstrate the effectiveness of incorporating multiple groundings and feedback. These findings underscore the critical importance of reasoning within the actual item space, rather than being confined to the language space, for recommendation tasks.

Problem

• Addresses reasoning-based recommendation deviations from real items
• Incorporates multiple grounding steps during inference for alignment
• Introduces user feedback mechanism to better adapt to interests

Innovation

• Multiple grounding rounds align reasoning with actual items
• User agent provides feedback during grounding steps
• Combines language reasoning with real item space