复现XRec模型，验证其生成推荐解释的有效性与稳定性。
Reproducibility Study of "XRec: Large Language Models for Explainable Recommendation"
- 用Llama 3替代GPT-3.5-turbo复现XRec框架，保持模型无关性。
- 引入协同信息后，解释生成更稳定，但未在所有指标上超越基线。
- 揭示专家混合模块嵌入对解释结构的关键影响，适合可解释推荐研究者参考。
本研究复现了Ma等（2024）提出的XRec——一种模型无关的协作式指令微调框架，使大语言模型（LLMs）能提供全面的推荐解释。我们使用Llama 3替代原论文中的GPT-3.5-turbo进行评估，并基于原始代码实现目标。通过修改或删除XRec的专家混合模块中的输入/输出嵌入，扩展分析其机制。结果表明，XRec能生成个性化解释，引入协同信息后提升稳定性；但在部分指标上未持续优于所有基线。分析揭示专家混合嵌入对解释结构的关键作用，凸显协同信号与语言建模的交互关系。我们开源完整实现，促进研究可复现性与社区共享。
原文摘要 · Abstract (English)
In this study, we reproduced the work done in the paper "XRec: Large Language Models for Explainable Recommendation" by Ma et al. (2024). The original authors introduced XRec, a model-agnostic collaborative instruction-tuning framework that enables large language models (LLMs) to provide users with comprehensive explanations of generated recommendations. Our objective was to replicate the results of the original paper, albeit using Llama 3 as the LLM for evaluation instead of GPT-3.5-turbo. We built on the source code provided by Ma et al. (2024) to achieve our goal. Our work extends the original paper by modifying the input embeddings or deleting the output embeddings of XRec's Mixture of Experts module. Based on our results, XRec effectively generates personalized explanations and its stability is improved by incorporating collaborative information. However, XRec did not consistently outperform all baseline models in every metric. Our extended analysis further highlights the importance of the Mixture of Experts embeddings in shaping the explanation structures, showcasing how collaborative signals interact with language modeling. Through our work, we provide an open-source evaluation implementation that enhances accessibility for researchers and practitioners alike. Our complete code repository can be found at https://github.com/julianbibo/xrec-reproducibility.
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