Consistency Regularization for Complementary Clothing Recommendations

[2411.12295] Consistency Regularization for Complementary Clothing Recommendations Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2411.12295 (cs) [Submitted on 19 Nov 2024] Title: Consistency Regularization for Complementary Clothing Recommendations Authors: Shuiying Liao , P.Y. Mok , Li Li View PDF HTML (experimental) Abstract: This paper reports on the development of a Consistency Regularized model for Bayesian Personalized Ranking (CR-BPR), addressing to the drawbacks in existing complementary clothing recommendation methods, namely limited consistency and biased learning caused by diverse feature scale of multi-modal data. Compared to other product types, fashion preferences are inherently subjective and more personal, and fashion are often presented, not by individual clothing product, but with other complementary product(s) in a well coordinated fashion outfit. Current complementary-product recommendation studies primarily focus on user preference and product matching, this study further emphasizes the consistency observed in user-product interactions as well as product-product interactions, in the specific context of clothing matching. Most traditional approaches often underplayed the impact of existing wardrobe items on future matching choices, resulting in less effective preference prediction models. Moreover, many multi-modal information based models overlook the limitations arising from various feature scales being involved. To address these gaps, the CR-BPR model integrates collaborative filtering techniques to incorporate both user preference and product matching modeling, with a unique focus on consistency regularization for each aspect. Additionally, the incorporation of a feature scaling process further addresses the imbalances caused by different feature scales, ensuring that the model can effectively handle multi-modal data without being skewed by any particular type of feature. The effectiveness of the CR-BPR model was validated through detailed analysis involving two benchmark datasets. The results confirmed that the proposed approach significantly outperforms existing models. Subjects: Information Retrieval (cs.IR) Report number: 115069 Cite as: arXiv:2411.12295 [cs.IR] (or arXiv:2411.12295v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2411.12295 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Volume 195, 2026 Related DOI : https://doi.org/10.1016/j.asoc.2026.115069 Focus to learn more DOI(s) linking to related resources Submission history From: Shuiying Liao [ view email ] [v1] Tue, 19 Nov 2024 07:30:07 UTC (11,994 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2024-11 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Consistency Regularization for Complementary Clothing Recommendations#670304
• SIREN (Luring LLMs onto the Rocks): PAIR-Driven Preference Manipulation in Web-RAG Recommenders#652403
• SIREN (Luring LLMs onto the Rocks): PAIR-Driven Preference Manipulation in Web-RAG Recommenders#650950
• On the Regularization Landscape for the Linear Recommendation Models#673540
• Learning from Conditionals#84981