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. This challenges the practical applicability of LLMs for recommendations. To address this, we propose a hybrid task allocation framework that utilizes the capabilities of both LLMs and traditional RSs. By adopting a two-phase approach to improve robustness to sub-populations, 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 and given to an LLM. We test our hybrid framework by incorporating various recommendation algorithms -- collaborative filtering and learning-to-rank recommendation models -- and two LLMs -- both open and close-sourced. Our results on three real-world datasets show a significant reduction in weak users and improved robustness of RSs to sub-populations $(\approx12\%)$ and overall performance without disproportionately escalating costs.

翻译：传统推荐系统（RSs）通常以统一优化所有训练样本的性能指标为目标。这使得数据驱动的推荐系统难以适应具有不同特性的多样化用户群体。不同人群之间的性能差异可能损害模型对子群体的鲁棒性。尽管近期研究在大语言模型（LLMs）适应推荐任务以处理困难样本方面取得了可喜成果，但来自数百万用户的长查询会降低LLMs性能，并导致成本、处理时间和推理延迟增加。这挑战了LLMs在推荐场景中的实际应用性。为此，我们提出一种混合任务分配框架，该框架同时利用LLMs和传统推荐系统的能力。通过采用两阶段方法提升对子群体的鲁棒性，我们实现了任务策略性分配，以促进LLMs高效且负责任的适应。该策略首先识别出推荐系统排序性能较差的弱用户与非活跃用户；随后，针对此类用户采用情境学习方法，将每位用户的交互历史构建为独立的排序任务，并交由LLM处理。通过整合多种推荐算法（包括协同过滤与学习排序推荐模型）以及两类LLM（开源与闭源），我们对混合框架进行了验证。在三个真实数据集上的实验结果表明，该方法显著减少了弱用户数量，将推荐系统对子群体的鲁棒性提升约12%，且在未过度增加成本的情况下实现了整体性能改进。