Optimal Transport Alignment of User Preferences from Ratings and Texts

Nhu-Thuat Tran, Hady W. Lauw

Keywords: textual content-aware recommendation, optimal transport alignment, disentangled interest factors

Abstract: 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.

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission271/Authors, auai.org/UAI/2025/Conference/Submission271/Reproducibility_Reviewers

Submission Number: 271