Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations

Understand

Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples.

• This makes it hard for data-driven RSs to cater to a diverse set of users due to the varying properties of these users.
• The performance disparity among various populations can harm the model's robustness with respect to sub-populations.
• While recent works have shown promising results in adapting large language models (LLMs) for recommendation to address hard samples, long user queries from millions of users can degrade the performance of LLMs and elevate costs, processing times and inference latency.