Uncertainty-Aware Explainable Recommendation with Large Language Models
This work addresses the problem of enhancing user trust and satisfaction in recommendation systems by providing tailored explanations, though it is incremental as it builds on existing prompt-tuning approaches for LLMs.
The paper tackles the challenge of generating text-based explanations in recommendation systems by developing a model that uses user and item ID vectors as prompts for GPT-2, achieving superior performance with metrics like 1.59 DIV on Yelp and 0.57 USR on TripAdvisor compared to SOTA methods.
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.