GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems

• URL: http://arxiv.org/abs/2506.04015v1
• Date: Wed, 04 Jun 2025 14:46:18 GMT
• Title: GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems
• Authors: Tiehua Mei, Hengrui Chen, Peng Yu, Jiaqing Liang, Deqing Yang,
• Abstract summary: Large language models (LLMs) have shown great potential in recommender systems.<n>GORACS is a novel Group-level Optimal tRAnsport-guided Coreset Selection framework for LLM-based recommender systems.
• Score: 17.1208625827132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although large language models (LLMs) have shown great potential in recommender systems, the prohibitive computational costs for fine-tuning LLMs on entire datasets hinder their successful deployment in real-world scenarios. To develop affordable and effective LLM-based recommender systems, we focus on the task of coreset selection which identifies a small subset of fine-tuning data to optimize the test loss, thereby facilitating efficient LLMs' fine-tuning. Although there exist some intuitive solutions of subset selection, including distribution-based and importance-based approaches, they often lead to suboptimal performance due to the misalignment with downstream fine-tuning objectives or weak generalization ability caused by individual-level sample selection. To overcome these challenges, we propose GORACS, which is a novel Group-level Optimal tRAnsport-guided Coreset Selection framework for LLM-based recommender systems. GORACS is designed based on two key principles for coreset selection: 1) selecting the subsets that minimize the test loss to align with fine-tuning objectives, and 2) enhancing model generalization through group-level data selection. Corresponding to these two principles, GORACS has two key components: 1) a Proxy Optimization Objective (POO) leveraging optimal transport and gradient information to bound the intractable test loss, thus reducing computational costs by avoiding repeated LLM retraining, and 2) a two-stage Initialization-Then-Refinement Algorithm (ITRA) for efficient group-level selection. Our extensive experiments across diverse recommendation datasets and tasks validate that GORACS significantly reduces fine-tuning costs of LLMs while achieving superior performance over the state-of-the-art baselines and full data training. The source code of GORACS are available at https://github.com/Mithas-114/GORACS.

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