DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems

Understand

The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements.

• However, this often comes at the cost of diminished recommendation diversity, which can negatively impact user satisfaction.
• To address this issue, controllable recommendation has emerged as a promising approach, allowing users to specify their preferences and receive recommendations that meet their diverse needs.
• Despite its potential, existing controllable recommender systems frequently rely on simplistic mechanisms, such as a single prompt, to regulate diversity-an approach that falls short of capturing the full complexity of user preferences.