Recommendation systems are ubiquitous, from Spotify playlist suggestions to Amazon product suggestions. Nevertheless, depending on the methodology or the dataset, these systems typically fail to capture user preferences and generate general recommendations. Recent advancements in Large Language Models (LLM) offer promising results for analyzing user queries. However, employing these models to capture user preferences and efficiency remains an open question. In this paper, we propose LLMRS, an LLM-based zero-shot recommender system where we employ pre-trained LLM to encode user reviews into a review score and generate user-tailored recommendations. We experimented with LLMRS on a real-world dataset, the Amazon product reviews, for software purchase use cases. The results show that LLMRS outperforms the ranking-based baseline model while successfully capturing meaningful information from product reviews, thereby providing more reliable recommendations.

翻译：推荐系统无处不在，从Spotify的播放列表推荐到亚马逊的产品推荐。然而，根据方法论或数据集的不同，这些系统通常难以捕捉用户偏好，并生成通用化的推荐。近来大语言模型（LLM）的进展为分析用户查询提供了有前景的结果。不过，如何利用这些模型来捕捉用户偏好并提升效率仍是一个开放性问题。本文提出LLMRS，一种基于LLM的零样本推荐系统，其中我们采用预训练LLM将用户评论编码为评论分数，并生成用户定制化的推荐。我们在真实数据集——亚马逊产品评论——上针对软件购买用例对LLMRS进行了实验。结果表明，LLMRS在从产品评论中成功捕捉有意义信息的同时，性能优于基于排序的基线模型，从而提供了更可靠的推荐。