The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectively integrating recommender system knowledge into LLMs for natural language generation which is tailored towards recommendation tasks remains a challenge. This paper addresses this challenge by making two key contributions. First, we introduce a new dataset (REGEN) for natural language generation tasks in conversational recommendations. REGEN (Reviews Enhanced with GEnerative Narratives) extends the Amazon Product Reviews dataset with rich user narratives, including personalized explanations of product preferences, product endorsements for recommended items, and summaries of user purchase history. REGEN is made publicly available to facilitate further research. Furthermore, we establish benchmarks using well-known generative metrics, and perform an automated evaluation of the new dataset using a rater LLM. Second, the paper introduces a fusion architecture (CF model with an LLM) which serves as a baseline for REGEN. And to the best of our knowledge, represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives. We demonstrate that LLMs can effectively learn from simple fusion architectures utilizing interaction-based CF embeddings, and this can be further enhanced using the metadata and personalization data associated with items. Our experiments show that combining CF and content embeddings leads to improvements of 4-12% in key language metrics compared to using either type of embedding individually. We also provide an analysis to interpret how CF and content embeddings contribute to this new generative task.

翻译：大型语言模型在生成与推理能力方面的最新进展，为开发真正对话式的推荐系统提供了机遇。然而，如何将推荐系统知识有效整合到大型语言模型中，以生成面向推荐任务的自然语言，仍然是一个挑战。本文通过两项关键贡献应对这一挑战。首先，我们为对话推荐中的自然语言生成任务引入了一个新数据集（REGEN）。REGEN（Reviews Enhanced with GEnerative Narratives）在亚马逊产品评论数据集的基础上扩展了丰富的用户叙事，包括对产品偏好的个性化解释、对推荐产品的背书以及用户购买历史的摘要。REGEN已公开提供，以促进进一步研究。此外，我们使用成熟的生成指标建立了基准，并利用一个评分大型语言模型对新数据集进行了自动化评估。其次，本文提出了一种融合架构（协同过滤模型与大型语言模型），作为REGEN的基线。据我们所知，这是首次尝试分析大型语言模型在理解推荐信号和生成丰富叙事方面的能力。我们证明，大型语言模型能够有效地从利用基于交互的协同过滤嵌入的简单融合架构中学习，并且可以通过使用与物品相关的元数据和个性化数据进一步增强这一能力。我们的实验表明，与单独使用任一类型的嵌入相比，结合协同过滤嵌入和内容嵌入可使关键语言指标提升4-12%。我们还提供了一项分析，以阐释协同过滤嵌入和内容嵌入如何对这一新的生成任务做出贡献。