ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
It addresses the need for explainable AI in recommendation systems, offering a novel approach that combines accuracy with interpretability, though it is incremental in leveraging existing LLM techniques.
This paper tackles the problem of generating personalized recommendations with human-interpretable explanations by introducing ReasoningRec, a framework that uses Large Language Models (LLMs) to model user preferences and aversions, resulting in a 12.5% improvement over state-of-the-art methods in recommendation prediction.
This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing on preferences, aversions, and explanatory reasoning. The framework utilizes a larger LLM to generate synthetic explanations for user preferences, subsequently used to fine-tune a smaller LLM for enhanced recommendation accuracy and human-interpretable explanation. Our experimental study investigates the impact of reasoning and contextual information on personalized recommendations, revealing that the quality of contextual and personalized data significantly influences the LLM's capacity to generate plausible explanations. Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5\% in recommendation prediction while concurrently providing human-intelligible explanations. The code is available here: https://github.com/millenniumbismay/reasoningrec.