  Text-based recommendation holds a wide range of practical applications due to
its versatility, as textual descriptions can represent nearly any type of item.
However, directly employing the original item descriptions may not yield
optimal recommendation performance due to the lack of comprehensive information
to align with user preferences. Recent advances in large language models (LLMs)
have showcased their remarkable ability to harness commonsense knowledge and
reasoning. In this study, we introduce a novel approach, coined LLM-Rec, which
incorporates four distinct prompting strategies of text enrichment for
improving personalized text-based recommendations. Our empirical experiments
reveal that using LLM-augmented text significantly enhances recommendation
quality. Even basic MLP (Multi-Layer Perceptron) models achieve comparable or
even better results than complex content-based methods. Notably, the success of
LLM-Rec lies in its prompting strategies, which effectively tap into the
language model's comprehension of both general and specific item
characteristics. This highlights the importance of employing diverse prompts
and input augmentation techniques to boost the recommendation effectiveness of
LLMs.
