  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.
