## 5 Conclusions  
This study focuses on the recommendation of fashion outfit combinations, specifically the pairing of tops and bottoms, by conducting in-depth data mining on price features and integrating image and textual information. We designed and implemented a multimodal deep learning recommendation model that incorporates price features, significantly enhancing the quality of outfit recommendations. The innovation of this study is reflected in two main aspects: first, by deeply exploring the impact of price features, especially when cross-referenced with other features, on user decision-making, and integrating these new features into the recommendation model; second, by combining the multi-head attention mechanism, the visual features, text features and price module features are effectively combined to combine the three modal features. The comparative experiments demonstrate that the DeepFMP model proposed in this study, along with the price feature extraction module, exhibit improvements across various evaluation metrics. This indicates that the two optimization points introduced in this paper are effective in the field of clothing outfit recommendation and provide a novel approach for personalized recommendation through multimodal fusion. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **AUC** & **Recall@5** & **Recall@10** & **NDCG@5** & **NDCG@10** \\ \hline Image, text & Base & 0.786 & 0.519 & 0.601 & 0.455 & 0.553 \\ Image, text & Base + attention & 0.802 & 0.536 & 0.622 & 0.476 & 0.585 \\ Image, text, price & Base & 0.816 & 0.545 & 0.635 & 0.483 & 0.596 \\ Image, text, price & Base + attention & 0.833 & 0.558 & 0.648 & 0.495 & 0.609 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experimental results of various recommendation models. Building on this, we summarized the main research contributions and findings, analyzed the practical application value, and proposed future research directions. The experimental results demonstrate significant performance improvements, not only validating the importance of price features but also providing insights for real-world applications. Testing on real user outfit data shows that the proposed multimodal recommendation model better aligns with user preferences. Particularly in price-sensitive scenarios, the model's recommendations more closely match consumer expectations. Furthermore, by analyzing the top-10 recommendations from the full dataset on randomly sampled test data, we observed a notable improvement in price alignment, which aligns with user consumption habits and research expectations. This can help businesses optimize pricing strategies and more accurately target their customer base. In future work, we plan to search for more diverse or larger datasets that can provide us with other dimensions of fashion clothing, such as fabric, material, color, and style, as well as factors like consumer age range, regional differences, cultural background, and social influences, which are also critical in outfit selection. These factors can be further mined and incorporated into feature design to enhance the personalization and accuracy of the model's recommendations. Additionally, clothing categories can be further subdivided. In this study, some categories were merged into two labels: tops and bottoms. While the recommendation results for popular top and bottom categories showed high accuracy and rationality, the results for niche categories or special styles need improvement. To address this, we aim to further refine clothing categories and design tailored feature extraction schemes to adapt to different clothing types, thereby improving the generalization capability of the recommendation system. Finally, for the pre-trained models used in the feature extraction module, with the development of deep learning and breakthroughs in hardware computing power, you can try to use more advanced pre-trained models [46] (such as Vit [47], Llama, etc.