# Attention-Based Personalized Compatibility Learning for Fashion Matching
## 2 Related Work  
The area of fashion compatibility learning and recommendation employs two main categories of algorithms: item-item level and multi-item level approaches. The former category models compatibility interaction between two items. For example, [PERSON] et al. [19]  
Figure 2: Overview of the proposed attention-based compatibility embedding network (PCE-Net) architecture. PCE-Net contains two parts: (1) attention-based compatibility embedding modeling based on feature extractor with two attention branches; (2) attention-based personal preference modeling based on matrix factorization and content-based inner product for user historical preferences. proposed the pairwise interaction tensor decomposition (PITF) factorization model, which simulates pairwise interactions between users, items, and tags. Meanwhile, refs. [20,21] utilized a single latent style space to measure compatibility solely using visual features. However, a single latent compatibility space is inadequate for the detailed modeling of complex relationships among different concepts, such as color, pattern, and category. Thus, to address this gap, [PERSON] et al. [1] proposed the Conditional Similarity Network (CSN) model, which learns different subspaces under different similarity metrics. [PERSON] et al. [5] then built on [1] by introducing a type-aware learning method to attain type-aware features in a universally shared potential space. Lastly, [PERSON] et al. [9] incorporated three attention mechanisms for multi-modal features in the type-aware subspace, building on the previous works. When predicting the compatibility between items, concatenation is adopted and implemented in a superior scheme. To automatically recognize the relative significance of different conditions, [PERSON] et al. [6] leveraged the attention mechanism [22]. In a different approach, [PERSON] et al. [23] suggested a graph neural network utilizing undirected graphs augmented with contextual information to predict associations between two items, thereby transforming the fashion compatibility issue into a graph edge detection problem. [PERSON] et al. [7] proposed a holistic approach to learning visual compatibility, encompassing TC-GAE, SAE, and search techniques modeled on a graph-based network, an autoencoder, and reinforcement learning, respectively. Finally, [PERSON] et al. [16] introduced Dual Autoencoder Network (DAE) as the first model to learn the compatibility feature space while incorporating the consistent relationship between visual-textual features and the implicit preference between items via Bayesian Personalized Ranking (BPR). [PERSON] et al. [24] presented AKD-DBPR, which employs knowledge distillation to combine fashion domain expertise with deep neural networks. [PERSON] et al. [25] introduced TransNFCM, a fashion neural compatibility model based on translations that aim to capture complex compatibility patterns via distance functions. [PERSON] et al. [26] developed a method for personalized outfit recommendation by training hash codes for both users and items and modeling users' preferences as the average of their preference scores for each item. [PERSON] et al. [11] proposed a joint model for general compatibility and personal preferences called GP-BPR, which combines the two characteristics. [PERSON] et al. [12] proposed a personalized recommendation modeling scheme named PAI-BPR, which utilizes attributes for interpretability and personalized recommendation. Finally, [PERSON] et al. [27] introduced PSA-Net, which learns attribute-wise visual feature subspaces via self-attention and incorporates customer embeddings to aid in recommending item pairs in a category-based subspace. The second category of outfit recommendation models aims to capture the interactions between multiple items in an outfit composed of three or more items. An early model developed by [PERSON] et al. [8] used a sequence approach, treating the items within an outfit as ordered, and proposed the Bi-LSTM sequence model.