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.

通过使用预训练的大型语言模型对用户评论进行编码并生成个性化推荐，我们提出了 LLMRS，一种基于 LLM 的零样本推荐系统。在真实世界的 Amazon 产品评论数据集上进行实验，结果表明 LLMRS 优于基于排名的基线模型，成功从产品评论中捕捉到有意义的信息，从而提供更可靠的推荐。