  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.
