Make Large Language Model a Better Ranker (2024)

Abstract: 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.

## introduction the rapid advancement in large language models (llms), known by @xcite , has marked a significant milestone in demonstrating their versatility in zero-shot and few-shot learning across various domains. these models, effectively employed in domains like question answering and information retrieval, have shown remarkable adaptability and reliability. their ability to efficiently handle tasks usually requiring extensive domain-specific training has sparked a surge in research aimed at exploring their potential across diverse applications, e.g. recommender system. ## problem statement we define the sequential recommendation ranking problem as follows. let u represent the set of users and i denote the set of items. for any given user @xmath0, their historical interactions with items are represented by h @xmath1, h 2 , . . . , h k }, where each h @xmath2. with this notation in place, the ranking problem is formalized as follows: definition 1 for a user u, consider c @xmath3, c 2 , . . . , c m } as the set of candidate items for recommendation, where each c @xmath4. the goal is to devise a ranking function f : h u × c @xmath5. the set s m is the symmetric group of all m-element permutations, encapsulating every possible arrangement of the candidate items. ## methodology in this section, we elucidate the constraints inherent in prevailing prompting paradigms when addressing list-wise recommendation tasks. our learning framework is developed with four distinct components: template design, supervised fine-tuning, soft lambda loss, and permutation-sensitive learning. ## experiment in our study, we conducted a comprehensive evaluation of our model across two real-world datasets. this was complemented by an ablation study, robustness tests, and efficiency evaluations. our experiment was directed by the following pivotal research questions: ## conclusion in this research, we tackled the intricacies of employing large language models as ranking agents in recommender systems with explicit feedback, focusing on refining list-wise ranking methods to manage the order relation. we proposed a cuttingedge framework that integrates soft lambda loss and permutation-sensitive learning. the integration of soft lambda loss is important as it bridges the objective between llm's natural language generation and the specific demands of ranking tasks. it enhances the performance of ranking by optimizing the order relation within the magnitude of ratings. furthermore, permutation-sensitive learning approaches effectively address the issue of posi-tion bias, providing an improvement over traditional bootstrapping methods without imposing additional computational demands during inference. our comprehensive evaluation across various datasets confirms the success of our method, advancing llms as recommendation agents.