ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models

Understand

Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services.

• However, existing recommenders face significant challenges in understanding the content of items.
• Large language models (LLMs), which possess deep semantic comprehension and extensive knowledge from pretraining, have proven to be effective in various natural language processing tasks.
• In this study, we explore the potential of leveraging both open- and closed-source LLMs to enhance content-based recommendation.