Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations
This work addresses the issue of equitable recommendations for diverse user groups, representing an incremental improvement by integrating LLMs into existing systems.
The paper tackles the problem of performance disparities in recommendation systems across diverse user populations by proposing a hybrid task allocation framework that uses large language models (LLMs) to assist weak and inactive users, resulting in a significant reduction in weak users and improved robustness without disproportionately escalating costs.
Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples, inadvertently overlooking the needs of diverse user populations. The performance disparity among various populations can harm the model's robustness to sub-populations due to the varying user properties. While large language models (LLMs) show promise in enhancing RS performance, their practical applicability is hindered by high costs, inference latency, and degraded performance on long user queries. To address these challenges, we propose a hybrid task allocation framework designed to promote social good by equitably serving all user groups. By adopting a two-phase approach, we promote a strategic assignment of tasks for efficient and responsible adaptation of LLMs. Our strategy works by first identifying the weak and inactive users that receive a suboptimal ranking performance by RSs. Next, we use an in-context learning approach for such users, wherein each user interaction history is contextualized as a distinct ranking task. We evaluate our hybrid framework by incorporating eight different recommendation algorithms and three different LLMs -- both open and close-sourced. Our results on three real-world datasets show a significant reduction in weak users and improved robustness to subpopulations without disproportionately escalating costs.