# Personalized Advertising Design Based on Automatic Analysis of an Individual's Appearance
## 4 Methodology  
Our proposed methodology, depicted in Figure 2, aims to generate personalized ads based on dress style. To achieve this, we followed several steps outlined below. 1. **Dataset creation**: We began by creating a dataset for the seven-style model. Rather than focusing on creating the largest possible dataset, we explored various deep learning approaches, as illustrated in Figure 1;
2. **Deep learning approaches**: We examined different deep learning approaches to predict the style of clothing. These approaches involved training supervised, semi-supervised, and transfer learning models using the created dataset. These models aim to accurately classify clothing into the seven universal styles;
3. **Expert system design**: Once the style was predicted, we designed an expert system that generates personalized ads based on the predicted style. The expert system takes into account the guidelines and characteristics associated with each style to create an ad that resonates with the individual's preferences and interests. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Methodology** & **Dataset** & **Findings/Results** \\ \hline [PERSON] [27] & Style evaluation model based on seven distinct types & - & Framework for categorizing clothing styles based on conveyed messages \\ \hline Various disciplines [28, 29] & Thorough scrutiny of the seven universal styles & - & Widely accepted model for evaluating apparent style \\ \hline [PERSON] et al. [41] & Utilized FashionNet with nearly one million annotated images & Images from diverse settings & Improved performance for deep fashion tasks \\ \hline [PERSON] et al. [44] & Introduced a double-path deep domain adaptation network & Street webcams and surveillance video images & Enhanced performance of convolutional neural network (CNN) \\ \hline [PERSON] et al. [45] & Trained CNN-like network with small dataset, expanded dataset with Internet images & Small dataset, additional Internet images & Neural network labeled up to 78\% of new images \\ \hline [PERSON] et al. [46] & Employed transfer learning and divide-and-conquer methodology & Clothing classification dataset & Significant 18\% improvement compared to previous architectures \\ \hline [PERSON] et al. [47] & Created dataset with different views and 264 descriptors & Dataset with different views of clothing & Achieved 80\% accuracy in clothing style recognition \\ \hline [PERSON] et al. [49] & Proposed a methodology for clothing image annotation & Clothing images with detected poses & Significant improvement in clothing image annotation \\ \hline Present study & Introduced deep learning model for style prediction and personalized ad generator & Images of participants in different styles & Achieved a top accuracy of 98.27\% for style prediction and 80.56\% acceptance rate for personalized ads \\ \hline \end{tabular}
\end{table} 
Table 1: Literature review: related works on clothing style recognition. Please note that, in Figure 1, red circles with letters indicate separate processes that we developed and divided into five parts, as outlined below:  
1. Creating the Style7 dataset: Using the FashionStyle14 dataset [50] as a basis, we generated a new set of images called the Style7 dataset;
2. Supervised training: We trained the Style7 architecture using supervised learning techniques, obtaining the first prediction result;
3. Semi-supervised approach: We employed a semi-supervised approach where we first trained the LIP autoencoder architecture using the LIP dataset [51]. Subsequently, we utilized the decoder component of this architecture, referred to as the ClassEmbedding2048 architecture. We fed the Style7 dataset into this model and obtained the second prediction result;
4. Transfer learning: We adopted a transfer learning approach by selecting four pre-trained architectures, which were initially trained by their respective authors using the ImageNet dataset [52].