# Attention-Based Personalized Compatibility Learning for Fashion Matching
## 1 Introduction  
The emergence of e-commerce has provided convenient shopping methods. However, the overload of data information caused by the vast amounts of products available on shopping websites has led to the need for a recommendation system to help users find items more quickly and accurately. We focus on developing an intelligent recommendation algorithm for clothing to address this demand. One of the primary challenges is generating reasonable matching suggestions for clothing styles and types. This requirement gives rise to the need for fashion compatibility modeling, which helps determine if fashion items meet specific matching criteria. As illustrated in Figure 1, compatible items satisfy particular rules, such as having matched colors and materials, while incompatible items violate those rules. Moreover, users have individual preferences, including style, texture, pattern, and more. For instance, user1 prefers pairing casual, loose tops with wide-legged pants, user 2 has a versatile fashion taste that ranges from casual sports to elegant dresses, and user 3 enjoys wearing clothes with striped patterns. Initially, some studies only considered visual features of fashion items [1; 2; 3; 4] when building comparison models. Subsequently, several works [5; 6; 7; 8] modeled compatibility by fusing both visual and textual multi-modal content. Further, researchers [5; 6; 9; 10], distinguished overall compatibility from fine-grained compatibility. Some recent studies [11; 12; 13; 14; 15], have considered user factors in personalized recommendation tasks. Although these workshave individual strengths, they fail to provide a comprehensive solution that addresses all the underlying problems. We aim to develop a personalized clothing recommendation system that takes visual and textual modalities as input, extracts fine-grained compatibility characteristics, and considers user preferences. In fashion recommendation, the primary challenge lies in accurately predicting and providing reasonable suggestions that align with a user's preferences. Researchers must concentrate on two significant issues: firstly, how to enhance the accuracy of determining fashion item compatibility from multi-modal data. Fashion recommendation is founded on the principle of fashion compatibility, which implies that various types of fashion items can be combined to create an outfit. Developing compatible feature spaces is crucial for continually advancing fashion compatibility models. For instance, researchers often employ visual features [1; 9], textual features [16], category-aware feature subspaces [5; 6], and neighbor node features of graph models [13; 17] as inputs. Secondly, since an outfit typically comprises multiple complementing fashion items, selecting items that satisfy the user's preferences while complementing each other is the crux of outfit construction. However, fashion is an inherently intricate and subjective concept, and defining fashion items frequently involves many complex intersubjective relationships between complementary fashion items. It is critical to note that the notion of compatibility between fashion items often spans categories and encompasses intricate interactions. To address the abovementioned challenges, our solution is an attention-based personalized compatibility embedding network called PCE-Net for clothing matching (Figure 2). This network can evaluate the compatibility between fashion items while capturing the user's personal preference from multi-modal features and their previous preferences. The two main components of the PCE-Net include attention-based compatibility embedding modeling and attention-based personal preference modeling. To overcome the first challenge, we introduce two attention branches to model fine-grained compatibility for the multi-modal data. To address the second challenge, we are inspired by the personal preference component of GP-BPR [11] and modeled attention-based personalized preference,  
Figure 1: Example of fashionable outfits from IQON3000.