  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.
