LLM as Explainable Re-Ranker for Recommendation System

TL;DR

This paper introduces a hybrid recommendation approach using large language models as explainable re-rankers, improving accuracy and interpretability over traditional models.

Contribution

It proposes a novel hybrid method combining traditional recommendation models with LLMs, enhancing ranking accuracy and explainability.

Findings

Significant improvement in NDCG scores

Outperforms zero-shot baseline in accuracy

Enhances interpretability of recommendations

Abstract

The application of large language models (LLMs) in recommendation systems has recently gained traction. Traditional recommendation systems often lack explainability and suffer from issues such as popularity bias. Previous research has also indicated that LLMs, when used as standalone predictors, fail to achieve accuracy comparable to traditional models. To address these challenges, we propose to use LLM as an explainable re-ranker, a hybrid approach that combines traditional recommendation models with LLMs to enhance both accuracy and interpretability. We constructed a dataset to train the re-ranker LLM and evaluated the alignment between the generated dataset and human expectations. Leveraging a two-stage training process, our model significantly improved NDCG, a key ranking metric. Moreover, the re-ranker outperformed a zero-shot baseline in ranking accuracy and interpretability. These results highlight the potential of integrating traditional recommendation models with LLMs to address limitations in existing systems and pave the way for more explainable and fair recommendation frameworks.