## 2 Related Work  
With the advancement of machine learning and deep learning, recommendation algorithms have progressively evolved from linear models to deep neural networks, incorporating personalized ranking and probabilistic modeling approaches. As a representative of generalized linear models, logistic regression was widely adopted in early recommendation systems for click-through rate (CTR) prediction tasks. While logistic regression demonstrates advantages in computational efficiency and strong interpretability, its inherent linear nature inherently limits automatic capture of feature interaction effects, necessitating the manual construction of cross-features. To overcome the limitations of linear models, [PERSON] [6] proposed the Factorization Machine (FM) model in 2010, which effectively models second-order feature interactions through latent vector inner products. This innovation significantly reduces manual feature engineering costs while maintaining robust generalization performance under sparse data conditions. Owing to its simplicity, efficiency, and scalability, the FM model has become one of the fundamental architectures in recommendation systems. The hybrid recommendation system architecture Wide&Deep, proposed by the Google team [7] in 2016, marked the advent of nonlinear deep models in recommendation systems by simultaneously achieving memorization and generalization capabilities. However, its Wide component still requires manual feature engineering, and the dual-pathway joint training may induce gradient conflicts. To address these limitations, [PERSON] et al. [8] developed the DeepFM model in 2017 through end-to-end learning optimization. This architecture integrates FM with deep neural networks (DNNs), sharing embedding layer parameters between both components to automatically capture feature interactions from second-order to higher-order levels. The DeepFM framework promotes the evolution of recommendation systems toward full automation while demonstrating exceptional architectural flexibility and extensibility. The rapid development of the fashion industry has spurred research on apparel recommendation algorithms, particularly in garment compatibility modeling. In 2019, [PERSON] et al. [9] proposed GP-BPR, a compatibility modeling framework for personalized clothing outfit recommendations. This framework employs ResNet50 for visual feature extraction while adopting a hybrid approach combining Word2 vec and textCNN for textual feature extraction, thereby pioneering a novel multimodal feature extraction methodology for fashion recommendation systems. Although this approach has been successfully applied to personalized outfit coordination scenarios, its relatively high computational complexity imposes limitations on large-scale industrial applications. Multimodal feature extraction is critical for fashion recommendations, encompassing visual, textual, and price features. Early methods relied on manual feature engineering, but advancements in pre-trained models and transfer learning now enable direct extraction of high-dimensional dense features. In computer vision, convolutional neural networks (CNNs), particularly ResNet [10], have excelled in extracting visual features like material, color, and texture [11]. [PERSON] et al. [12] tackled the cross-domain clothing retrieval problem by integrating the ResNet50 model with a quadruplet loss function. [PERSON] et al. [13] proposed integrating the Convolutional Block Attention Module (CBAM) into ResNet50 to improve feature extraction capabilities for garment regions, leading to a notable increase in retrieval accuracy. Recent work by [PERSON] et al. [14] and [PERSON] et al. [15] further enhanced feature extraction through multi-level fusion and attention mechanisms. In natural language processing (NLP), word embedding techniques have evolved from static representations like Word2 Vec [16] to contextual models like BERT [7; 17], which captures nuanced linguistic relationships. [PERSON] et al.