Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2025

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.

Keywords

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

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

UAI '25: Proceedings of the Forty-First Conference on Uncertainty in Artificial Intelligence, Rio de Janeiro Brazil, July 21-25

First Page

4251

Last Page

4265

Identifier

10.5555/3762387.3762574

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.5555/3762387.3762574

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