% We study the problem of disentangling and aligning user interest factors from ratings and textual content using the optimal transport (OT) framework. By treating interest factors from both modalities as discrete distributions, we frame their alignment as an OT problem, allowing us to discover the optimal alignment probabilities between these factors in a data-driven manner. Then, we introduce \ourmethod, a novel model that discovers then aligns user interest factors from ratings and texts via optimal transport. \ourmethod\ not only aligns these factors but also facilitates the transference of preference signals between them through two key mechanisms: an alignment probability-guided regularization term and a barycentric mapping strategy. These two mechanisms enables \ourmethod\ to integrate signals from both ratings and texts when disentangling user preferences, leading to more accurate textual content-aware recommendations.
We propose a novel model \ourmethod\ to align disentangled user interests from ratings and texts. By treating interest factors as distributions, we frame their alignment as an optimal transport problem, enabling data-driven discovery of probabilistic correspondences. \ourmethod's novelty features two key mechanisms: (1) an alignment probability-guided regularization term and (2) a barycentric mapping strategy. These enable \ourmethod\ to integrate cross-modal interest signals, improving textual content-aware recommendation accuracy and offering interpretable alignment of user preferences.