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