LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Authors: name: Hanjia Lyu, name: Song Jiang, name: Hanqing Zeng, name: Yinglong Xia, name: Jiebo Luo

We investigate various prompting strategies for enhancing personalized
content recommendation performance with large language models (LLMs) through
input augmentation. Our proposed approach, termed LLM-Rec, encompasses four
distinct prompting strategies: (1) basic prompting, (2) recommendation-driven
prompting, (3) engagement-guided prompting, and (4) recommendation-driven +
engagement-guided prompting. Our empirical experiments show that combining the
original content description with the augmented input text generated by LLM
using these prompting strategies leads to improved recommendation performance.
This finding highlights the importance of incorporating diverse prompts and
input augmentation techniques to enhance the recommendation capabilities with
large language models for personalized content recommendation.