<p>Multi-Target Prediction (MTP) aims to predict a class for multiple targets from a single input instance. In this paper, we focus on the general ordinal outputs setting, where targets may have different numbers of ordered classes. This scenario is under-explored yet critical for human-centered applications such as educational assessment or psychological profiling. In these domains, targets span multiple aspects of an evaluation, and capturing latent relationships between seemingly independent targets is essential for deriving meaningful user profiles. Beyond prediction accuracy, it is crucial that the resulting profiles are interpretable, as these applications directly impact humans. We introduce IMPACT, a novel method that extends existing binary MTP frameworks (such as CD-BPR) to the multi-class domain through a newly designed loss function. Rooted in a Bayesian modeling framework, IMPACT jointly embeds user profiles and targets within a shared vector space, providing theoretical rigor while explicitly optimizing for both predictive accuracy and interpretability. Furthermore, IMPACT offers a geometric interpretation of the embedding learning dynamics, giving insight into how the model captures relationships between users and targets and providing an intuitive understanding of profile formation in the latent space. Experimental results show that IMPACT outperforms state-of-the-art approaches in terms of profile interpretability while maintaining competitive prediction accuracy. An ablation study highlights the contribution of each component, demonstrating the benefits of extending the framework to multiple ordered classes, the Bayesian formulation, and the geometric interpretability in enhancing both performance and transparency.</p>