## 1 Introduction  
According to Statista's projections, the global apparal market is expected to reach USD 1.79 trillion by 2024, with an annual compound growth rate of 2.81% from 2024 to 2028. The apparal industry is a significant sector in economic development, particularly with the rapid growth in online consumption in recent years, making intelligent clothing shopping a key trend in the fashion retail industry [1]. Intelligent clothing shopping systems typically utilize personalized recommendation algorithms, virtual fitting technologies, and data-driven analytics to meet users' personalized needs. Especially in the era of big data, with the explosive growth in data, recommendation systems have become a crucial technology [2] in clothing consumption. E-commerce platforms can collect vast amounts of data on users' purchase histories, browsing records, search keywords, and product click-through rates, enabling businesses to effectively conduct personalized marketing and enhance user experiences.  
Simultaneously, the rapid rise of international social platforms, such as Xiaohongshu, Instagram, and TikTok, has led to an increasing number of people sharing their outfit photos and clothing combinations on these platforms, showcasing personal styles. This trend has not only transformed shopping habits but also provided a rich data source for clothing brands and retailers. Supported by big data and recommendation algorithms, businesses can extract value from user-generated content (UGC), analyze similarities between users and clothing products, and thereby create more detailed user profiles, improve personalized recommendation accuracy, enhance user engagement, strengthen brand loyalty, and increase purchase rates.  
In the context of the big data era, collaborative filtering algorithms recommend items to users by calculating item similarities, while content-based recommendation systems make recommendations by analyzing the match between item content features and users' historical preferences [3]. In the deep learning context, convolutional neural networks are used to extract information from images and texts for relevant recommendations. However, current recommendation systems often fail to account for consumers' price sensitivity and consumption preferences, which vary significantly across individuals. Some consumers prioritize cost-effectiveness [4], while others focus on fashion or brand appeal. This gap limits the ability to provide truly personalized recommendations that align with users' financial expectations and preferences.  
To address these limitations, we propose DeepFMP, a novel multimodal recommendation framework that integrates textual descriptions, image features, and price information. By leveraging a deep learning-based approach, DeepFMP captures the complex interactions between these modalities, providing more accurate and personalized outfit recommendations. Our framework not only meets the visual requirements of fashion coordination but also aligns with consumers' reasonable price expectations, thereby enhancing the effectiveness and accuracy of recommendation systems [5]. The main contributions of this paper are summarized as follows:  
1. From the perspective of price sensitivity, a price feature extraction module was designed to capture characteristics such as clothing brand, category, and user historical behavior, enabling the model to provide recommendations aligned with users' consumption preferences.
2. By leveraging transfer learning, pre-trained models were introduced into the domain of clothing outfit recommendation, enhancing the depth and breadth of feature representation in the model's initial input vectors.
3. In the context of multimodal recommendation, we propose an effective recommendation framework that integrates a multi-head attention mechanism into the multimodal recommendation algorithm. This approach explores optimized methods for interaction and fusion across different modalities, thereby improving the model's ability to learn from diverse modal features.