Finetuning Large Language Model for Personalized Ranking

TL;DR

This paper introduces DMPO, a new framework that improves large language models' ability to perform personalized recommendations by optimizing preference alignment, showing significant performance gains in real-world datasets and cross-domain scenarios.

Contribution

The paper proposes DMPO, a novel training framework that enhances LLMs for recommendation tasks by optimizing preference probabilities, addressing data disparity issues.

Findings

DMPO outperforms traditional recommendation methods in few-shot scenarios.

DMPO demonstrates superior cross-domain recommendation capabilities.

The case study highlights DMPO's potential as an explainable recommendation system.

Abstract

Large Language Models (LLMs) have demonstrated remarkable performance across various domains, motivating researchers to investigate their potential use in recommendation systems. However, directly applying LLMs to recommendation tasks has proven challenging due to the significant disparity between the data used for pre-training LLMs and the specific requirements of recommendation tasks. In this study, we introduce Direct Multi-Preference Optimization (DMPO), a streamlined framework designed to bridge the gap and enhance the alignment of LLMs for recommendation tasks. DMPO enhances the performance of LLM-based recommenders by simultaneously maximizing the probability of positive samples and minimizing the probability of multiple negative samples. We conducted experimental evaluations to compare DMPO against traditional recommendation methods and other LLM-based recommendation approaches. The results demonstrate that DMPO significantly improves the recommendation capabilities of LLMs across three real-world public datasets in few-shot scenarios. Additionally, the experiments indicate that DMPO exhibits superior generalization ability in cross-domain recommendations. A case study elucidates the reasons behind these consistent improvements and also underscores DMPO's potential as an explainable recommendation system.