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：大语言模型（LLMs）在各个领域展现出强大能力，引发了LLM增强推荐系统（RS）的范式转变。现有研究主要集中于逐点式和成对式推荐范式，这些范式因计算成本高昂而在基于LLM的推荐器中效率低下。然而，现有的列表式方法也因排序目标与下一词预测之间的错位而在排序任务中表现不足。此外，这些基于LLM的方法难以有效处理候选项目间的序关系，特别是在评分规模较大的情况下。为应对这些挑战，本文提出了具有对齐列表排序目标（ALRO）的大语言模型框架。ALRO旨在弥合LLM能力与排序任务精细需求之间的差距。具体而言，ALRO通过引入软lambda损失——一种为优化序关系而定制的lambda损失适配方法，以列表形式利用显式反馈。该机制提供了更精确的优化目标，从而增强排序过程。此外，ALRO整合了置换敏感学习机制，能够解决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计算负担。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及基于LLM的推荐基线模型。