## 4 Experiments
### Non-Label-Given Completion  
First, we conduct experiments for the non-label-given task, where the category of the missing item is not given. For training, we use category matching to obtain the predicted \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **\#Pairs** & **Categories** \\ \hline \hline \multirow{3}{*}{Street2 Shop} & \multirow{3}{*}{2533} & footwear, dresses, tops, skirts, \\  & & pants, leggings, outerwear, \\  & & bags, hats, belts, eyewear \\ \hline \multirow{3}{*}{STL-Fashion} & \multirow{3}{*}{9619} & shoes, shirts\&tops, shorts, \\  & & skirts, pants, coats\&jackets, \\ \cline{1-1}  & & handbags\&wallets\&cases, sunglasses, necklaces, earrings \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data statistics after pre-processing. The underlined category is handled separately or not used by our method. set \(\pi\), from which the negative item is sampled. For evaluation, the ground-truth item \(i\) of each query scene \(s\) is compared with items from all the categories. **Binary Accuracy and AUC. ** Tables 3 and 4 show Binary Accuracy (BA) and AUC for all the models on two datasets. To compute BA, the ground-truth item only compares with one randomly sampled item, while for AUC, the ground-truth item is compared with every item in the dataset. Hence AUC is more dependable and stable. From the table, we can conclude that the method without visual features (CF) and the method without training (ImageNet Features) are not comparable to others. OCTP is our model without using body-shape compatibility, which can already outperform our strongest baseline CTL. BOCTP can further improve OCTP under all settings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**STL-Fashion**} \\ \cline{3-6}  & & \multicolumn{3}{c}{**All**} & **Clothes** \\ \cline{3-6}  & & **BA** & **AUC** & **BA** & **AUC** \\ \hline a & CF & 0.5192 & 0.5055 & 0.4609 & 0.4563 \\ \hline b & \
\begin{tabular}{c} ImageNet \\ Features \\ \end{tabular} & 0.5078 & 0.5114 & 0.5354 & 0.5172 \\ \hline c & IBR & 0.5723 & 0.5477 & 0.5638 & 0.5410 \\ \hline d & DVBPR & 0.5015 & 0.5010 & 0.5496 & 0.5133 \\ \hline e & ViBE & 0.6116 & 0.5613 & 0.6315 & 0.5609 \\ \hline f & CTL & 0.6357 & 0.6223 & 0.6418 & 0.5993 \\ \hline g & OCTP (ours) & 0.6940 & 0.6630 & 0.6843 & 0.6552 \\ \hline h &
\begin{tabular}{c} BOCTP \\ (ours) \\ \end{tabular} & **0.6982** & **0.6725** & **0.7269** & **0.6667** \\ \hline imp & h vs f & 9.83\% & 8.06\% & 13.25\% & 11.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Binary Accuracy (BA) and AUC on STL-Fashion dataset for non-label-given outfit completion. Under the dataset name, ‘all’ denotes the whole dataset which includes clothes, shoes and accessories, while ‘clothes’ only contains clothing items. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Stree2 Shop**} \\ \cline{3-6}  & & \multicolumn{3}{c}{**All**} & **Clothes** \\ \cline{3-6}  & & **BA** & **AUC** & **BA** & **AUC** \\ \hline a & CF & 0.4861 & 0.4876 & 0.4421 & 0.4543 \\ \hline b & \
\begin{tabular}{c} ImageNet \\ Features \\ \end{tabular} & 0.5019 & 0.5020 & 0.4842 & 0.4620 \\ \hline c & IBR & 0.5256 & 0.5207 & 0.5578 & 0.5402 \\ \hline d & DVBPR & 0.5533 & 0.5284 & 0.5684 & 0.5473 \\ \hline e & ViBE & 0.6007 & 0.5613 & 0.6526 & 0.5526 \\ \hline f & CTL & 0.6284 & 0.5944 & 0.6631 & 0.6082 \\ \hline g & OCTP (ours) & 0.6442 & 0.6078 & 0.6842 & 0.6096 \\ \hline h &
\begin{tabular}{c} BOCTP \\ (ours) \\ \end{tabular} & **0.6719** & **0.6147** & **0.7263** & **0.6568** \\ \hline imp & h vs f & 6.92\% & 3.41\% & 9.53\% & 7.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Binary Accuracy (BA) and AUC on Street2 Shop dataset for non-label-given outfit completion. Under the dataset name, ‘all’ denotes the whole dataset which includes clothes, shoes and accessories, while ‘clothes’ only contains clothing items. **HR and NDCG**. Figure 7 shows the results of HR and NDCG on 'all' datasets.