Large Language Models (LLMs) demonstrate robust capabilities across various fields, leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date focuses on point-wise and pair-wise recommendation paradigms, which are inefficient for LLM-based recommenders due to high computational costs. However, existing list-wise approaches also fall short in ranking tasks due to misalignment between ranking objectives and next-token prediction. Moreover, these LLM-based methods struggle to effectively address the order relation among candidates, particularly given the scale of ratings. To address these challenges, this paper introduces the large language model framework with Aligned Listwise Ranking Objectives (ALRO). ALRO is designed to bridge the gap between the capabilities of LLMs and the nuanced requirements of ranking tasks. Specifically, ALRO employs explicit feedback in a listwise manner by introducing soft lambda loss, a customized adaptation of lambda loss designed for optimizing order relations. This mechanism provides more accurate optimization goals, enhancing the ranking process. Additionally, ALRO incorporates a permutation-sensitive learning mechanism that addresses position bias, a prevalent issue in generative models, without imposing additional computational burdens during inference. Our evaluative studies reveal that ALRO outperforms both existing embedding-based recommendation methods and LLM-based recommendation baselines.

翻译：大型语言模型（LLMs）在各个领域展现出强大的能力，引发了LLM增强推荐系统（RS）的范式转变。迄今为止的研究主要集中在点对点和成对推荐范式上，由于计算成本高昂，这些范式对于基于LLM的推荐器效率低下。然而，现有的列表式方法也因排序目标与下一个词元预测之间的错位而在排序任务中存在不足。此外，这些基于LLM的方法难以有效处理候选项目之间的顺序关系，尤其是在评分规模较大的情况下。为了应对这些挑战，本文提出了具有对齐列表排序目标（ALRO）的大型语言模型框架。ALRO旨在弥合LLM能力与排序任务细致要求之间的差距。具体而言，ALRO通过引入软lambda损失（一种为优化顺序关系而定制的lambda损失变体），以列表方式利用显式反馈。该机制提供了更精确的优化目标，从而增强了排序过程。此外，ALRO整合了一种对排列敏感的学习机制，该机制解决了生成模型中普遍存在的位置偏差问题，且无需在推理阶段增加额外的计算负担。我们的评估研究表明，ALRO在性能上超越了现有的基于嵌入的推荐方法以及基于LLM的推荐基线。