LLM-Rec: Personalized Recommendation via Prompting Large Language Models

arXiv:2307.15780v3 Announce Type: replace
Abstract: Text-based recommendation holds a wide range of practical applications due to its versatility, as textual descriptions can represent nearly any type of item. However, directly employing the original item descriptions may not yield optimal recommendation performance due to the lack of comprehensive information to align with user preferences. Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning. In this study, we introduce a novel approach, coined …

abstract applications arxiv cs.ai cs.cl cs.ir however information language language models large language large language models llm performance personalized practical prompting recommendation text textual type via