  Traditionally, most of the existing attribute learning methods are trained
based on the consensus of annotations aggregated from a limited number of
annotators. However, the consensus might fail in settings, especially when a
wide spectrum of annotators with different interests and comprehension about
the attribute words are involved. In this paper, we develop a novel multi-task
method to understand and predict personalized attribute annotations. Regarding
the attribute preference learning for each annotator as a specific task, we
first propose a multi-level task parameter decomposition to capture the
evolution from a highly popular opinion of the mass to highly personalized
choices that are special for each person. Meanwhile, for personalized learning
methods, ranking prediction is much more important than accurate
classification. This motivates us to employ an Area Under ROC Curve (AUC) based
loss function to improve our model. On top of the AUC-based loss, we propose an
efficient method to evaluate the loss and gradients. Theoretically, we propose
a novel closed-form solution for one of our non-convex subproblem, which leads
to provable convergence behaviors. Furthermore, we also provide a
generalization bound to guarantee a reasonable performance. Finally, empirical
analysis consistently speaks to the efficacy of our proposed method.
