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.

翻译：在推荐系统中提供解释能够提升用户满意度并增强信任感，尤其是通过阐述为用户定制推荐项目的原因。该领域的主流方法围绕生成文本解释展开，其中重点关注大语言模型的应用。然而，由于时间限制和计算资源限制，针对可解释推荐微调大语言模型在实践中并不可行。作为替代方案，当前方法侧重于训练提示词而非大语言模型本身。在本研究中，我们开发了一个模型，将用户和项目的ID向量作为GPT-2的提示词输入。我们采用多任务学习框架内的联合训练机制，同时优化推荐任务和解释任务。该策略能够更有效地探索用户兴趣，提升推荐效果和用户满意度。通过实验，我们的方法在Yelp、TripAdvisor和Amazon数据集上分别达到1.59 DIV、0.57 USR和0.41 FCR，在可解释性评估指标方面优于四种现有最优方法。此外，我们确认所提模型能够在三个公开数据集上保持稳定的文本质量。