Uncertainty-Aware Explainable Recommendation with Large Language Models

TL;DR

This paper introduces a novel approach for explainable recommendations using prompt-based training of GPT-2 with user and item ID vectors, enhancing recommendation quality and explainability without extensive LLM fine-tuning.

Contribution

The study proposes a multi-task learning framework that trains prompts for GPT-2 using ID vectors, improving explainability and recommendation effectiveness efficiently.

Findings

Achieved superior explainability metrics on Yelp, TripAdvisor, and Amazon datasets.

Demonstrated stable textual quality across multiple datasets.

Outperformed four state-of-the-art methods in explainability evaluation.

Abstract

Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tailored to the user. The predominant approach in this domain revolves around generating text-based explanations, with a notable emphasis on applying large language models (LLMs). However, refining LLMs for explainable recommendations proves impractical due to time constraints and computing resource limitations. As an alternative, the current approach involves training the prompt rather than the LLM. In this study, we developed a model that utilizes the ID vectors of user and item inputs as prompts for GPT-2. We employed a joint training mechanism within a multi-task learning framework to optimize both the recommendation task and explanation task. This strategy enables a more effective exploration of users' interests, improving recommendation effectiveness and user satisfaction. Through the experiments, our method achieving 1.59 DIV, 0.57 USR and 0.41 FCR on the Yelp, TripAdvisor and Amazon dataset respectively, demonstrates superior performance over four SOTA methods in terms of explainability evaluation metric. In addition, we identified that the proposed model is able to ensure stable textual quality on the three public datasets.