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

TL;DR

This paper introduces a hybrid task allocation framework that adaptively and responsibly enhances large language models for recommendation systems, improving robustness and equity across diverse user groups while managing costs.

Contribution

It presents a novel two-phase task allocation method that identifies weak users and employs in-context learning to improve recommendation fairness and robustness.

Findings

Significant reduction in weak user groups.

Enhanced robustness to diverse subpopulations.

Maintained cost-effectiveness.

Abstract

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.