## 4 Results and Discussions
### Experimental Settings  
#### 4.1.1 Dataset  
Currently, most existing datasets for clothing outfit composition are constructed by collecting data from outfit-sharing websites, such as FashionVC, ExpFashion, Polyvore Dataset, and IQON3000. The specific research problem addressed in this study involves utilizing users' historical behavior and garment information for top-bottom outfit matching and recommendation. However, the FashionVC dataset is too small in scale, the ExpFashion dataset primarily contains user review texts about complete outfits rather than individual items, and the Polyvore Dataset contains an excessive number of items per outfit composition. Furthermore, none of these three datasets provide user information, which does not align with the requirements of this research. To better analyze the performance of clothing outfit matching models, this study ultimately selects the IQON3000 dataset. This study utilizes the IQON3000 dataset, which was collected by [PERSON] et al. from the popular fashion website IQON [9]. On this platform, users upload images of their outfits, which can be liked and commented on by others, creating an interactive mechanism based on user behavior. This provides rich information for analyzing user preferences in the dataset. The IQON3000 dataset comprises 308,747 outfit records uploaded by 3568 users, involving 672,335 fashion items [9], as shown in Table 1.  
Each outfit not only includes tops and bottoms but also accessories, footwear, and other components, comprehensively showcasing the diversity and complexity of clothing combinations. Most importantly, the IQON3000 dataset prov \begin{table}
\begin{tabular}{c c} \hline \hline
**Item Category** & **Quantity** \\ \hline Outwear & 35,765 \\ Tops & 119,895 \\ Bottom & 77,813 \\ Dresses & 25,816 \\ Shoes & 106,598 \\ Accessories & 306,448 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of dataset IQON3000. individual fashion items, offering a data foundation for training the model in terms of price sensitivity. The composition of an outfit in the dataset is illustrated in Figure 3.  
In this study, we focus exclusively on the combination of tops and bottoms. Therefore, based on the category labels in the dataset, we filtered out items that belong to the top and bottom categories, discarding data from other unused categories, and retained the data as the original clothing dataset. Additionally, to facilitate the data mining of price-related information, we preserved the brand, color, price, and category labels of the clothing items in the dataset. To extract textual and visual features of the clothing, we also retained the image information and product descriptions provided in the dataset. The proposed clothing outfit recommendation model employs a training approach based on implicit feedback. We constructed triplets for training purposes. The construction process is illustrated in Figure 4:  
In the dataset, each user possesses a set of top-bottom outfit combinations \(O_{u}=\{(t_{1},b_{1}),(t_{2},b_{2}),\ldots,(t_{m},b_{n})\}\). By aggregating the outfit combinations of all users, we obtain a comprehensive set of tops \(T=\{t_{1},t_{2},\ldots,t_{M}\}\) and a set of bottoms \(B=\{b_{1},b_{2},\ldots,b_{n}\}\). For each user u's actual outfit combination \((t_{m},b_{n})\), we perform global random negative sampling for both tops and bottoms. Specifically, we randomly select negative samples \(t_{j}\) and \(b_{k}\) from the complete sets T and B, respectively. These negative samples  
Figure 4: Construction of negative samples for the training dataset. Figure 3: One outfit in the IQON3000 dataset. are then paired with the original positive samples to generate two negative sample pairs: \(\left\{(t_{m},b_{k}),\left(t_{j},b_{n}\right)\right\}\). The negative sample pairs are labeled as 0, while the positive sample pairs are labeled as 1, thereby creating the dataset required for training [39].