## 4 Results and Discussions
### Result Analysis and Performance Comparison
#### 4.2.3 Comparative Test Case Study  
To facilitate a clearer comprehension of the tasks involved in this study, enable a more intuitive analysis of the final recommendation outcomes of our model, and compare the performance of our model with that of others, this section presents the top 10 successfully retrieved samples randomly selected from the test set, displaying the recommended list of apparel images. The comparative Top-10 recommendation results of the DeepFMP model with the added price feature module are illustrated in Figure 7, where the garment highlighted by a red box represents the actual matching sample chosen by the user for the respective top.  
As illustrated in Figure 7, for the same upper garment query, the proposed model ranks positive sample lower garments in higher positions within the recommendation sequence. The red bounding box in the figure highlights the positive sample lower garment. It can be observed that the price ranges of the tops and bottoms actually chosen by users are relatively close, with the total cost remaining within 1000. In the recommendation list generated by the model incorporating the price feature module, the prices of the recommended items are quite similar, aligning well with the user's current consumption habits. In contrast, the Top-10 recommendation list obtained after removing the price module from the model, although not significantly different in terms of garment appearance, exhibits a  
Figure 7: Comparative experiment: visualization of ranking results.  
considerable fluctuation in price range. Some individual items far exceed the price range of the actual matching, potentially surpassing the user's actual price tolerance range. This demonstrates the effectiveness of the proposed price feature extraction module in the actual recommendation results and its significant impact on the personalization and accuracy of the recommendations.  
Following the integration of the price feature module, a comparison of the Top-10 recommendation results between DeepFMP and GP-BPR is illustrated in Figure 8. As shown in Figure 8, for the same upper garment query, the proposed model ranks positive sample lower garments higher in the recommendation sequence. The red box highlights the positive sample. It can be observed that, from the perspective of aesthetic coordination, the recommendation outcomes of both models are relatively aligned with the general public's aesthetic preferences, presenting reasonably coordinated ensembles. Notably, the Top-5 recommendation list generated by the proposed DeepFMP model successfully includes the actual matching samples chosen by users, with positive samples positioned more prominently in the recommendation list. This indicates that our model exhibits higher accuracy in apparel recommendation. Furthermore, from the standpoint of price features, the proposed model demonstrates a superior capability in uncovering latent factors related to pricing. Additionally, it achieves commendable recommendation efficacy for high-priced long-tail items, showcasing robust performance.  
Figure 8: Price module comparative experiments: visualization of ranking results.