  Conventional multi-label recognition methods often focus on label confidence,
frequently overlooking the pivotal role of partial order relations consistent
with human preference. To resolve these issues, we introduce a novel method for
multimodal label relevance ranking, named Label Relevance Ranking with Proximal
Policy Optimization (LR\textsuperscript{2}PPO), which effectively discerns
partial order relations among labels. LR\textsuperscript{2}PPO first utilizes
partial order pairs in the target domain to train a reward model, which aims to
capture human preference intrinsic to the specific scenario. Furthermore, we
meticulously design state representation and a policy loss tailored for ranking
tasks, enabling LR\textsuperscript{2}PPO to boost the performance of label
relevance ranking model and largely reduce the requirement of partial order
annotation for transferring to new scenes. To assist in the evaluation of our
approach and similar methods, we further propose a novel benchmark dataset,
LRMovieNet, featuring multimodal labels and their corresponding partial order
data. Extensive experiments demonstrate that our LR\textsuperscript{2}PPO
algorithm achieves state-of-the-art performance, proving its effectiveness in
addressing the multimodal label relevance ranking problem. Codes and the
proposed LRMovieNet dataset are publicly available at
\url{https://github.com/ChazzyGordon/LR2PPO}.
