% Uncovering hidden factors driving user preferences is a growing area of research. However, the limited availability of rating data often exacerbates this direction. Previous studies have attempted to address this by aligning the preference factors identified from textual content with those derived from ratings. While this approach has shown improvements, a couple of limitations remain. First, because the alignment information between the preference factors from ratings and texts is unavailable, assuming a fixed one-to-one correspondence between these factors, as done in previous works, can lead to suboptimal performance. Second, existing methods for transferring preference signals between ratings and texts have not been sufficiently explored, limiting their potential to improve recommendation accuracy.
% To address, we propose an optimal transport alignment approach. By treating preference factors derived from ratings and texts as supporting points of two discrete distributions, we compute an optimal transport plan as the probabilistic relationship between these distributions. The resultant transport plan, regarded as alignment matrix, is then used not only to guide a regularization term but also in barycentric mapping, enabling the blending and transferring interest signals between rating and text-based preference factors. Extensive experiments on real-world datasets demonstrate the superiority of our method over competitive baselines.
Modeling hidden factors driving user preferences is crucial for recommendation yet challenging due to sparse rating data. While aligning preference factors from ratings and texts, as a solution, shows improvements, existing methods impose restrictive one-to-one factor correspondences and underutilize cross-modal interest signals. We propose an optimal transport (OT) approach to address these gaps. By modeling rating- and text-based preference factors as distributions, we compute an OT plan that captures their probabilistic relationships. This plan serves dual roles: 1) to regularize cross-modal preference factors without rigid correspondence assumptions, and 2) to blend preference signals across modalities through barycentric mapping. Experiments on real-world datasets validate our method’s effectiveness over competitive baselines, highlighting its novel use of OT for adaptive preference factor alignment, an underexplored direction in recommender system research.