LLM-Rec: Personalized Recommendation via Prompting Large Language Models

Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Jiebo Luo

LLM-Rec: Personalized Recommendation via Prompting Large Language Models: 28 upvotes on Hugging Face Daily Papers, #4 of 13 papers on 2023-08-01. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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