End-to-end Training for Recommendation with Language-based User Profiles

Understand

There is a growing interest in natural language-based user profiles for recommender systems, which aims to enhance transparency and scrutability compared with embedding-based methods.

• Existing studies primarily generate these profiles using zero-shot inference from large language models (LLMs), but their quality remains insufficient, leading to suboptimal recommendation performance.
• In this paper, we introduce LangPTune, the first end-to-end training framework to optimize LLM-generated user profiles.
• Our method significantly outperforms zero-shot approaches by explicitly training the LLM for the recommendation objective.