## 3 The Proposed Model
### Feature Extraction and Embedding
#### 3.1.3 Price Feature Extraction and Embedding  
In the process of mining clothing price data, to enhance the precision of the analysis, this study constructed a series of multi-dimensional price features. These features include the price of individual clothing items, such as the price of tops, bottoms, and the combined price of top and bottom sets, as well as the mean and standard deviation of the prices of tops, bottoms, and combined sets from the user's last 10 historical outfit records [32]. Additionally, the mean and standard deviation of prices related to clothing categories were introduced to further delineate the impact of clothing categories on price distribution [33]. To delve deeper into users' consumption preferences, we also extracted historical average price features for brands and categories, thereby capturing users' price preference patterns across different brands and categories. In the specific implementation process, the extraction of price features is shown in Figure 2:  
(1) Single item price feature  
\[P_{\text{top}}=\text{price}(\text{top}),P_{\text{bottom}}=\text{price}(\text{ bottom})\]  
where \(P_{\text{top}}\) and \(P_{\text{bottom}}\) represent the prices of the top and bottom clothing items, respectively. (2) Combination Price Feature  
\[P_{\text{combo}}=P_{\text{top}}+P_{\text{bottom}}\]  
where \(P_{\text{combo}}\) denotes the combined price of the top and bottom clothing items. (3) Price Statistical Features from Historical Outfit Records  
Figure 2: Price feature extraction. For the user's last 10 historical outfit records, the mean and standard deviation of the prices for tops, bottoms, and combinations are calculated as follows:  
\[\mu_{\text{top}}=\frac{1}{N}{\sum_{i=1}^{N}P_{\text{top}}^{(i)}},\sigma_{\text{ top}}=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}\left(P_{\text{top}}^{(i)}-\mu_{\text{top}} \right)^{2}}}\]  
where \(\mu_{\text{top}}\) and \(\sigma_{\text{top}}\) represent the mean and standard deviation of the top prices, respectively [5], \(P_{\text{top}}^{(i)}\) denotes the price of the top in the \(i\)-th outfit record, and \(N\) is the number of historical outfit records. The same formulas apply to the bottom and combination prices. These features capture the user's recent price preferences and acceptance levels in clothing selection, while the standard deviation reflects the user's budget consistency or interest in different price ranges across various outfits. (4) Category Price Mean and Standard Deviation  
For each clothing category, the mean and standard deviation of prices for all items within that category are calculated as follows:  
\[\mu_{\text{category}}=\frac{1}{M}{\sum_{i=1}^{M}P_{\text{category}}^{(i)}},\sigma_ {\text{category}}=\sqrt{\frac{1}{M}{\sum_{i=1}^{M}\left(P_{\text{category}}^{(i)}- \mu_{\text{category}}\right)^{2}}}\]  
where \(\mu_{\text{category}}\) and \(\sigma_{\text{category}}\) represent the mean and standard deviation of prices within the category, respectively, \(P_{\text{category}}^{(i)}\) denotes the price of the \(i\)-th item in the category, and \(M\) is the total number of items in the category. These metrics reflect the overall pricing level and price stability within the category, providing insights into the general pricing trends and variability for the specific clothing category. (5) Brand Price Mean and Standard Deviation  
\[\mu_{\text{brand}}(b)=\frac{1}{K_{b}}{\sum_{i=1}^{K_{b}}P_{\text{brand}}^{(i)}},\sigma_{\text{brand}}(b)=\sqrt{\frac{1}{K_{b}}{\sum_{i=1}^{K_{b}}\left(P_{ \text{brand}}^{(i)}-\mu_{\text{brand}}(b)\right)^{2}}}\]  
where \(\mu_{\text{brand}}(b)\) and \(\sigma_{\text{brand}}(b)\) represent the mean and standard deviation of prices for brand \(b\), respectively, \(P_{\text{brand}}^{(i)}\) denotes the price of the \(i\)-th clothing item under brand \(b\), and \(K_{b}\) is the total number of clothing items under brand \(b\).