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.

翻译：本文报告了一种用于贝叶斯个性化排序的一致性正则化模型（CR-BPR）的开发，该模型旨在解决现有互补服装推荐方法中的缺陷，即多模态数据特征尺度差异导致的有限一致性和有偏学习。与其他产品类型相比，时尚偏好本质上是主观且更具个人化的，时尚通常不是以单个服装产品的形式呈现，而是与其他互补产品共同构成协调的时尚搭配。当前的互补产品推荐研究主要关注用户偏好和产品匹配，本研究进一步强调在服装搭配这一特定情境下，用户-产品交互以及产品-产品交互中观察到的一致性。大多数传统方法往往低估了现有衣橱单品对未来搭配选择的影响，导致偏好预测模型效果欠佳。此外，许多基于多模态信息的模型忽视了不同特征尺度所带来的局限性。为弥补这些不足，CR-BPR模型整合了协同过滤技术，将用户偏好建模和产品匹配建模相结合，并特别注重对每个方面进行一致性正则化。此外，通过引入特征缩放处理，进一步解决了不同特征尺度造成的不平衡问题，确保模型能够有效处理多模态数据，而不会因任何特定类型的特征而产生偏差。CR-BPR模型的有效性通过基于两个基准数据集的详细分析得到了验证。结果证实，所提出的方法显著优于现有模型。