Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation

Understand

Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.

• Recently, large language models (LLMs) have gained prominence for their capabilities in understanding and reasoning over textual semantics, and have found utility in various domains, including recommendation.
• Conventional recommendation methods and LLMs each have their strengths and weaknesses.
• While conventional methods excel at mining collaborative information and modeling sequential behavior, they struggle with data sparsity and the long-tail problem.