## 4 Experiments
### Visualization  
We visualize the results of our methods and the two strongest baselines to showcase the benefits of our methods. Figure 13 shows predictions of BOCTP on various item categories. Figure 11: Ablation study on STL-Fashion ’all’ dataset for label-given task. Binary Accuracy and AUC on clothes, shoes and accessories are shown in the figure. Figure 12: Binary Accuracy (BA) per category on STL-Fashion dataset for the label-given task. Figure 10: Ablation study on Street2 Shop ’all’ dataset for label-given task. Binary Accuracy and AUC on clothes, shoes, accessories and dresses are shown in the figure. The most compatible results are consistent with the ground-truth item and the query scene, which proves BOCTP's ability for item compatibility learning. Figure 14 shows the results on dresses, which are more body-specific than others and can better evaluate the body awareness of the model. The body type of the subject in column (a) is curvy. BOCTP can recommend A-line dresses with pleats, which are suitable for pear-shaped bodies. The subject in column (b) has an average body type, and BOCTP is able to find similar items to the ground truth in terms of style, color and pattern. For column (c), the subject is tall and slender, and BOCTP recommends skinny dresses and peplum dresses as they flatter people with an hourglass figure. On the other hand, the results from CTL are close to random guesses on dresses. ViBE is able to give some body-compatible recommendations, but it cannot predict the item features correctly. The results of OCTP and BOCTP are more consistent with the ground-truth items, and BOCTP can make a more precise prediction about the style of the dresses since it is able to capture the body-shape information of the subject. Figure 15 shows a comparison of OCTP and BOCTP when recommending items from different categories to further illustrate the superiority of BOCTP. Please note that all the scenes used for visualization are sampled from the testing set. Due to the inaccuracy of the  
Figure 14: Visualizations of the top 5 recommendations of four methods on dresses for 3 subjects with typical body shapes. Each line is the top 1 to 5 (left to right) recommendation results of one method for 3 subjects, respectively. Figure 13: Visualizations of the top 3 most and least compatible items predicted by BOCTP on different categories of items. datasets, sometimes the ground-truth item may look a little bit different from the query item in the full scene (e.g., different colors of the same style). This will not affect the model training too much. For column (a) in Figure 15, BOCTP gives priority to tight sling tops since the subject is slim. As for column (b), the subject has a pear-shaped body type, and knee ripped jeans can make the subject's legs look straight and thin. Although the 1 st and 4 th recommendations from OCTP are also jeans, they are more suitable for slender people to wear. Meanwhile, the body type of the model can serve as a reference. We can observe that the models who wear the jeans recommended by BOCTP are of a regular or curvy body type, which are similar to the model appearing in the ground-truth item image. On the contrary, the models in the 1 st and 4 th recommendations of OCTP are more slender. The subject in column (c) is curvy; curved bags such as buckets (the 1 st recommendation of OCTP) will make her look plump. On the contrary, the top-ranked recommendations, especially the top 3 from BOCTP, are stiff flap bags with chains, which are more suitable for apple-shaped bodies. To conclude, the items recommended by OCTP are more diverse in view of style, while BOCTP selects specific items for the subject based on the body information and can achieve higher accuracy. Please note that when we analyze body shape-aware recommendations, we first consider the suitableness between items and body types. Some people may prefer items that are not suitable for their body types. For example, it is possible that the subject in column (b) prefers the 1 st recommendation of OCTP to BOCTP.