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.

翻译：传统推荐系统通常以优化所有训练样本上的性能指标为目标，无意中忽视了多样化用户群体的需求。由于用户属性各异，不同群体间的性能差异可能损害模型对子群体的稳健性。尽管大型语言模型在提升推荐系统性能方面展现出潜力，但其实际应用受到高成本、推理延迟以及对长用户查询性能下降的阻碍。为应对这些挑战，我们提出一种混合任务分配框架，旨在通过公平服务所有用户群体来促进社会效益。通过采用两阶段方法，我们推动任务的策略性分配，以实现LLMs的高效且负责任的适应。我们的策略首先识别出那些在推荐系统中获得次优排序性能的弱势和非活跃用户。随后，对此类用户采用上下文学习方法，将每位用户的交互历史情境化为独立的排序任务。我们通过整合八种不同的推荐算法和三种不同的LLM（包括开源和闭源模型）来评估混合框架。在三个真实世界数据集上的实验结果表明，该方法显著减少了弱势用户数量，并提升了对子群体的稳健性，且未造成成本的不成比例增加。