Legislative voting records are widely used in political sciences to characterize revealed preferences among the member of a deliberative assembly. In this context, item-response models (a class of latent factor models) such as NOMINATE and IDEAL are the preeminent quantititive tools used for analysis. This class of models assumes that member\'s choices can be explained by continuous latent features, often called ideal points. For unidimensional latent spaces, this often results in a ranking of members along the liberal-conservative spectrum. In contrast, this paper introduces a modeling approach for legislative voting records that uses discrete latent features to account for the differential propensities of members to vote for different bills. One of the byproducts of our model is a classification of bills into clusters, which are characterized not by the language in the bill but by the voting patterns of legislators. In addition, the model is extended to account for informative missingness in the form of strategic absences. The model is illustrated using two real-life examples.