# Attention-Based Personalized Compatibility Learning for Fashion Matching
## 4 Experiment
### Results and Discussion  
We consider the following baselines in the top-bottom pairs recommendation experiments to evaluate the proposed model. * **POP-T:** POP is frequently used as a baseline in recommender systems [49]. POP-T simply selects the most popular bottoms for each top and vice versa. Here, \"popularity\" is defined as the number of tops paired with the bottom, i.e., the number of top-bottom pairs in the training set. * **POP-U:** For this baseline [49], the number of users that used this bottom as a component of an outfit in the training set is used to determine the \"popularity\" of the bottom. * **RAND:** The compatibility ratings between positive and negative pairs were randomly assigned. * **Bi-LSTM:** The bidirectional LSTM method in [8] modeled an outfit as an ordered sequence. Its operating principle is to predict the next item conditioned on previous ones. We keep the variables constant by setting the sequence length to 2, i.e., there are only a top and a bottom. * **BPR-DAE:** The baseline [16] uses a dual autoencoder network (DAE) to learn the potential compatibility space by jointly modeling the consistent relationship between visual and textual patterns and the implicit preference between items by Bayesian Personalized Ranking. * **BPR-MF:** This model [18] is one of the most commonly used techniques for personalized recommendation tasks, which captures the latent user-item interaction by the Matrix Factorization (MF) method in the pairwise ranking tasks. * **VBPR:** Unlike MF, the model [2] also considers the user's preference for visual factors. The model represents the visual characteristics of an outfit by averaging the visual characteristics of the items in the set. * **TBPR:** The difference between TBPR [2] and VBPR is using information from different modalities. * **VTBPR:** This model [2] uses a combination of both visual and text modalities to model user preferences. * **GP-BPR:** This baseline [11] combines visual and textual features of clothing with personal preferences to jointly model general (item-item) compatibility and personal (user-item) preferences, where matrix factorization for the user-item interaction matrix is performed to obtain the potential user preferences. * **PAI-BPR:** This model [12] is an attribute-based interpretable personal preference modeling scheme, where personalization is achieved by taking inspiration from GP-BPR [11] and adding attribute-wise interpretable results. Since the code is not publicly available, we directly report the experimental results of Table 3 in the original paper [12] for quantitative comparison. The performance comparison of various techniques is shown in Table 1. These quantitative data allow us to draw the following conclusions:  
* BPR-DAE outperforms Bi-LSTM, demonstrating that the content-based model, which captures the compatibility relationship between items by directly extracting features from multimodal data, is superior to the sequential model (predicting the following item from the previous one). * VTBPR performs better than VBPR, TBPR, and BPR-MF, indicating the value of multimodal data in enhancing model performance. * To solve the problem of personalized clothing matching, GP-BPR and PAI-BPR combine generalized item-item compatibility and user-item preferences using multi-modal characteristics. Since PAI-BPR uses an attribute classification network to address the interpretability of the model, performance has been slightly improved. * PCE-NET obtains the best performance compared to the above baseline, but there is no modeling attribute classification module because PCE-NET does not focus on interpretability problems. Our model can automatically capture the compatibility features of multi-modal data using two attention branches separately, which indicates that further development and exploitation of multi-modal data is necessary for embedding learning tasks. **Qualitative Results.