{"ID":2879361,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16210","arxiv_id":"2508.16210","title":"Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings","abstract":"Cross-domain recommender (CDR) systems aim to transfer knowledge from data-rich domains to data-sparse ones, alleviating sparsity and cold-start issues present in conventional single-domain recommenders. However, many CDR approaches rely on overlapping users or items to establish explicit cross-domain connections, which is unrealistic in practice. Moreover, most methods represent user preferences as fixed discrete vectors, limiting their ability to capture the fine-grained and multi-aspect nature of user interests. To address these limitations, we propose DUP-OT (Distributional User Preferences with Optimal Transport), a novel framework for non-overlapping CDR. DUP-OT consists of three stages: (1) a shared preprocessing module that extracts review-based embeddings using a unified sentence encoder and autoencoder; (2) a user preference modeling module that represents each user's interests as a Gaussian Mixture Model (GMM) over item embeddings; and (3) an optimal-transport-based alignment module that matches Gaussian components across domains, enabling effective preference transfer for target-domain rating prediction. Experiments on Amazon Review datasets show that DUP-OT outperforms single-domain baselines even without source-domain data, and achieves lower RMSE than the cross-domain baseline TDAR under strictly non-overlapping training settings, demonstrating its effectiveness in reducing large prediction errors for cold-start users. The implementation is available at https://github.com/XiaoZY2000/dup-ot.","short_abstract":"Cross-domain recommender (CDR) systems aim to transfer knowledge from data-rich domains to data-sparse ones, alleviating sparsity and cold-start issues present in conventional single-domain recommenders. However, many CDR approaches rely on overlapping users or items to establish explicit cross-domain connections, whic...","url_abs":"https://arxiv.org/abs/2508.16210","url_pdf":"https://arxiv.org/pdf/2508.16210v3","authors":"[\"Ziyin Xiao\",\"Toyotaro Suzumura\"]","published":"2025-08-22T08:32:13Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":610580,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2879361,"paper_url":"https://arxiv.org/abs/2508.16210","paper_title":"Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings","repo_url":"https://github.com/XiaoZY2000/dup-ot","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}