LLM-Rec: Personalized Recommendation via Prompting Large Language Models

Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Chris Leung, Ren Chen, Jiajie Tang, Jiebo Luo

Keywords: recommendation, large language model, input augmentation, personalization

Abstract: 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 as input features may not yield optimal recommendation performance. This limitation arises because these descriptions often lack comprehensive information that can be effectively exploited 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 investigate diverse prompting strategies aimed at $\textit{augmenting the input text}$ to enhance personalized text-based recommendations. Our novel approach, coined $\textbf{LLM-Rec}$, encompasses four distinct prompting techniques: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that incorporating the augmented input text generated by the LLMs yields discernible improvements in recommendation performance. Notably, the recommendation-driven and engagement-guided prompting strategies exhibit the capability to tap into the language model's comprehension of both general and personalized item characteristics. This underscores the significance of leveraging a spectrum of prompts and input augmentation techniques to enhance the recommendation prowess of LLMs.

Primary Area: general machine learning (i.e., none of the above)

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Submission Number: 2988