Generating user-friendly explanations regarding why an item is recommended
has become increasingly common, largely due to advances in language generation
technology, which can enhance user trust and facilitate more informed
decision-making when using online services. However, existing explainable
recommendation systems focus on using small-size language models. It remains
uncertain what impact replacing the explanation generator with the recently
emerging large language models (LLMs) would have. Can we expect unprecedented
results?
In this study, we propose LLMXRec, a simple yet effective two-stage
explainable recommendation framework aimed at further boosting the explanation
quality by employing LLMs. Unlike most existing LLM-based recommendation works,
a key characteristic of LLMXRec is its emphasis on the close collaboration
between previous recommender models and LLM-based explanation generators.
Specifically, by adopting several key fine-tuning techniques, including
parameter-efficient instructing tuning and personalized prompt techniques,
controllable and fluent explanations can be well generated to achieve the goal
of explanation recommendation. Most notably, we provide three different
perspectives to evaluate the effectiveness of the explanations. Finally, we
conduct extensive experiments over several benchmark recommender models and
publicly available datasets. The experimental results not only yield positive
results in terms of effectiveness and efficiency but also uncover some
previously unknown outcomes. To facilitate further explorations in this area,
the full code and detailed original results are open-sourced at
this https URL

通过使用大型语言模型 (LLMs)，我们提出了简单而有效的两阶段解释性推荐框架 LLMXRec，旨在进一步提高解释质量。与大多数现有的基于 LLM 的推荐系统不同，LLMXRec 侧重于先前推荐模型与基于 LLM 的解释生成器之间的紧密协作。通过采用一些关键的微调技术，包括参数高效的指导微调和个性化提示技术，我们能够生成可控且流畅的解释，以实现解释推荐的目标。我们提供了三个不同的角度来评估解释的有效性，并在几个基准推荐模型和公开可用的数据集上进行了大量实验。实验证据不仅在有效性和效率方面取得了积极的结果，还揭示了一些以前未知的结果。为了方便在这个领域的进一步探索，我们开源了完整的代码和详细的原始结果。

发掘大型语言模型在可解释推荐中的潜力

Unlocking the Potential of Large Language Models for Explainable  Recommendations

Interpretable machine learning tackles the important problem that humans
cannot understand the behaviors of complex machine learning models and how
these models arrive at a particular decision. Although many approaches have
been proposed, a comprehensive understanding of the achievements and challenges
is still lacking. We provide a survey covering existing techniques to increase
the interpretability of machine learning models. We also discuss crucial issues
that the community should consider in future work such as designing
user-friendly explanations and developing comprehensive evaluation metrics to
further push forward the area of interpretable machine learning.

本文综述了提高可解释机器学习模型的技术，并讨论了未来研究的关键问题，如设计用户友好的解释和开发全面的评估指标，以进一步推动可解释机器学习的发展。