Aligning Large Language Models with Recommendation Knowledge

Understand

Large language models (LLMs) have recently been used as backbones for recommender systems.

• However, their performance often lags behind conventional methods in standard tasks like retrieval.
• We attribute this to a mismatch between LLMs' knowledge and the knowledge crucial for effective recommendations.
• While LLMs excel at natural language reasoning, they cannot model complex user-item interactions inherent in recommendation tasks.