LLM-Rec: Personalized Recommendation via Prompting Large Language Models
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.
64c86550be8b04314ca0cb2c
Hanjia Lyu
claimed_verified
2023-08-01T18:23:27.621Z
64c939307dba66c3a7e4d215
/avatars/0b662cc1799525188476f3e6e1f97d29.svg
BruceLyu
user
brucelyu
64c86550be8b04314ca0cb2d
Song Jiang
claimed_verified
2023-08-02T20:30:28.299Z
629ff655198a9a5a7562d437
/avatars/42fd229a1521dc218c1fb385dd867b3b.svg
Song Jiang
user
songjiang
64c86550be8b04314ca0cb2e
Hanqing Zeng
claimed_verified
2024-11-15T09:54:36.121Z
6729196729fdd2b3fc360e21
/avatars/407f0660c8b2b63bf2b60aa9553bb2c6.svg
Hanqing Zeng
user
zimplex
64c86550be8b04314ca0cb2f
Yinglong Xia
64c86550be8b04314ca0cb30
Jiebo Luo
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
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.
Combining diverse prompts and input augmentation techniques improves personalized content recommendation performance using large language models.
large language models LLM-Rec basic prompting recommendation-driven prompting engagement-guided prompting input augmentation recommendation performance