# Attention-Based Personalized Compatibility Learning for Fashion Matching
## 4 Experiment
### Ablation Study  
**Different Modalities. ** We further assess the contribution of different input modalities in our model, specifically, the two variants of PCE-Net: PCE-Net-V and PCE-Net-T. PCE-Net-V utilizes only the visual modality to extract compatibility features, while PCE-Net-T focuses on the textual modality. Table 2 presents the performance of these modalities when used as inputs for PCE-Net. To provide a more precise comparison, we also offer the experimental results of the optimal baselines (GP-BPR [11] and PAI-BPR [12]) using only a single modality. Based on the findings in Table 2, we make the following observations: (1) PCE-Net-V and PCE-Net-T outperform GP-BPR-V and PAI-BPR-V, and GP-BPR-T and PAI-BPR-T, respectively. This validates the effectiveness of the attention branches we introduced to enhance model performance for different modalities. (2) PCE-Net performs better than both PCE-Net-V and PCE-Net-T, indicating that utilizing both modalities as complementary information improves the learning of compatibility embedding and enhances personalized preference modeling. (3) Interestingly, we note that model-T outperforms model-V in the GP-BPR [11] and PAI-BPR [12] baselines. The authors argue that critical features like pattern, style, and brand can be better summarized in the textual information. For instance, fashion items are more likely to be compatible if they share the same brand. However, our model PCE-Net-V attains equivalent performance to PCE-Net-T and even slightly outperforms the latter, suggesting that the visual attention branch effectively captures compatibility features automatically and enhances personalized preference modeling. In Equation (13), the non-negative parameter \(\pi\) denotes the weight assigned to the visual modality. Based on the aforementioned conclusions, it is imperative to incorporate multiple modalities concurrently into the model. Thus, Figure 7 presents a line graph depicting the model's performance at various values of \(\pi\). As the figure reveals, our model achieves optimal performance when \(\pi=0.5\), indicating that both modalities hold equal importance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Modality** & **Approach** & **AUC** \\ \hline \multirow{3}{*}{Visual modality} & GP-BPR-V & 0.8239 \\  & PAI-BPR-V & 0.8413 \\  & PCE-Net-V & 0.8485 \\ \hline \multirow{3}{*}{Textual modality} & GP-BPR-T & 0.8313 \\  & PAI-BPR-T & 0.8432 \\  & PCE-Net-T & 0.8475 \\ \hline \multirow{3}{*}{Muti-modal} & GP-BPR & 0.8314 \\  & PAI-BPR & 0.8502 \\ \cline{1-1}  & PCE-Net & 0.8534 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison among different modalities in terms of AUC. Figure 7: Performance of PCE-Net with respect to the trade-off parameter \(\pi\), \(\pi=0.5\) is the best. Attention Branch.The Attention Branch plays a crucial role in our study. To evaluate its impact on the model across different modalities, we present quantitative data in Table 3. The findings highlight that employing two separate attention branches to encode the preprocessing features of visual and textual modalities effectively captures compatible interaction features. Furthermore, these features serve as complementary data, ultimately enhancing the model's overall performance. Different Component.To evaluate the individual contributions of each component in our model, we present the results in Table 4, specifically focusing on two components: compatibility embedding modeling and personal preference modeling within PCE-Net. Our observations are as follows: (1) Our comprehensive model surpasses the performance of the two derived models containing only one component. This substantiates the vital role each component plays in our model. (2) PCE-Net-P outperforms PCE-Net-C, signifying that users' historical preferences effectively capture their personalized preferences, thereby influencing the outcome of the personalized clothing matching task.