LLM-Rec：透過促使大型語言模型進行個性化推薦

摘要

我們研究了各種提示策略，以增強大型語言模型（LLMs）通過輸入擴充來提高個性化內容推薦的性能。我們提出的方法名為LLM-Rec，包括四種不同的提示策略：（1）基本提示，（2）推薦驅動提示，（3）參與引導提示，以及（4）推薦驅動+參與引導提示。我們的實證實驗表明，將原始內容描述與LLM生成的擴增輸入文本結合，使用這些提示策略可提高推薦性能。這一發現凸顯了將多樣的提示和輸入擴充技術納入大型語言模型以增強個性化內容推薦能力的重要性。

English

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.

LLM-Rec：透過促使大型語言模型進行個性化推薦

LLM-Rec: Personalized Recommendation via Prompting Large Language Models

摘要

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