ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning

TL;DR

ReasoningRec uses large language models to improve personalized recommendations and generate human-understandable explanations, outperforming existing methods by up to 12.5% in accuracy.

Contribution

This work introduces a novel framework that leverages LLMs for joint recommendation and explanation generation, bridging the gap between AI predictions and human interpretability.

Findings

Outperforms state-of-the-art methods by up to 12.5% in recommendation accuracy.

Enables generation of human-interpretable explanations alongside recommendations.

Shows the importance of contextual and personalized data quality for explanation plausibility.

Abstract

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.