LLM-Rec: Personalized Recommendation via Prompting Large Language Models
This work addresses the challenge of improving personalized recommendations for users in text-based systems, but it is incremental as it builds on existing LLM capabilities with novel prompting strategies.
The paper tackles the problem of suboptimal performance in text-based recommendation due to insufficient item descriptions, and introduces LLM-Rec, a method using prompting strategies for text enrichment, which significantly enhances recommendation quality, with basic MLP models achieving comparable or better results than complex content-based methods.
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.