# Attention-Based Personalized Compatibility Learning for Fashion Matching
## 4 Experiment
### Results and Discussion
** To visually demonstrate the superiority of our model compared with GP-BPR [11] in personalized clothing matching and to analyze the impact of each component in our model, we present multiple sets of test samples along with the model's evaluation of positives and negatives in Figure 3. As previously mentioned, the testing quadruplet \((m,i,j,k)\) indicates the user \(u_{m}\) prefers the bottom \(b_{j}\) than \(b_{k}\) for the given top \(t_{i}\). In the first example involving user1, our model determines that the bottom \(b_{k}\) is more compatible with \(t_{i}\) compared to \(b_{j}\). Further analysis reveals that our model can capture the historical preference of users for brown, skirts, which stems from the contribution of the visual attention branch. In contrast, GP-BPR [11] captures users' preference for short skirts. Even though our model predicts incorrectly, we can see that there is a visually compatible relationship between \(t_{i}\) and \(b_{k}\), which indicates that our model is capable of generating some convincing matching suggestions. In the second example, our model learns better than GP-BPR for visual features of fashion items. In the first example involving user 3, the bottom \(b_{j}\) and \(b_{k}\) both have similar styles of clothing in the user's historical preferences, which may lead PCE-Net-P to predict \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Approach** & **AUC** \\ \hline \multirow{8}{*}{Baselines} & POP-T & 0.6042 \\  & POP-U & 0.5951 \\  & RAND & 0.5014 \\  & Bi-LSTM & 0.6675 \\  & BPR-DAE & 0.7004 \\  & BPR-MF & 0.7913 \\  & VBPR & 0.8129 \\  & TBPR & 0.8146 \\  & VTBPR & 0.8213 \\  & GP-BPR & 0.8314 \\  & PAI-BPR & 0.8502 \\ \hline Proposed & PCE-Net & 0.8534 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison among different approaches in terms of AUC. Figure 3: Illustration of the method comparison and the influence of the attention-based compatibility embedding and personal preference model-ing. All the quadruplets satisfy the ground truth that \(\{u_{m},t_{i}\}:b_{i}>b_{k}\). PCENET-C and PCENET-P denotes the above two components, respectively. “\(\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{ \check{\check{\check{\check{\checkcheck{\  
incorrectly. However, by considering the fine-grained compatibility relationships between fashion items from PCE-Net-C, black and white pairing is more common in the matching rules, and white clothing is more versatile in the matching results, i.e., the matching degree is well, so PC-E-Net finally obtains the correct evaluation result. In the second example, the top \(t_{i}\) and the bottom \(b_{k}\) are indistinguishable in terms of visual features such as color, which leads to an incorrect prediction of PCE-Net-C. At this point, component P (i.e., Personal Preference Modeling) enables PCE-Net to obtain the correct prediction result by capturing the user's historical preferences. Above, we have demonstrated that our model outperforms other baseline models to a certain extent. Additionally, both components of our model are indispensable and serve as complementary sources of information to enhance its overall performance. To assess the learning ability of the proposed PCE-Net, we visually analyze the compatibility relation for positive and negative pairs, as well as the resulting compatibility embedding space. **Compatible Relations Visualization. ** Figure 4 illustrates the application of t-SNE [50] to represent the learned compatible relation space. Each dot in the plot represents the multi-modal fusion feature of a top-bottom pair. The red dots represent compatible top-bottom pairs, while the blue dots represent incompatible pairs. The separation of the two relations learned by PCE-Net indicates that our model effectively distinguishes whether a compatible relationship exists between the top-bottom pairs and produces convincing matching results. We observe an intriguing occurrence of crossover points between the red and blue regions.