# A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and Images
## 6 Conclusions and Future Work  
To solve the data sparsity issues in the recommender systems, previous studies have extracted various features from online reviews to integrate them into models. Since online reviews contain rich and specific user preference information, they enhance recommendation performance with clear motivations. Although online reviews contain multimodal information, including texts and images, many studies focus solely on textual information, which overlooks the informational value of images. Regarding the notion of each modality providing different information, previous studies have been limited in considering the complementary or substitutive effect of different modalities. To target this research gap, this study proposes a novel recommender system with a co-attention mechanism to incorporate the complementarity between texts and images. The experimental results were evaluated using two datasets from Amazon, and the proposed CAMRec model outperformed baseline models. This implies that the complementary fusion of texts and images significantly contributes to enhancing recommendation power. Moreover, the CAMRec model is proposed to have scalability, and can efficiently process large-scale datasets with superior performance. This ensures the model is well-suited for real-world applications, specifically in environments where data volumes continuously increase. Therefore, this study provides a new perspective in the field of recommender systems and demonstrates that our proposed model enhances recommendation performance while being adaptable to scalable and large-scale implementations. The theoretical implications of this study are as follows. First, this study attempted to fuse textual and visual features, which has not yet been performed in previous studies. Therefore, this study provides a new direction and extends the scope of recommender system-related studies. Second, this study provides significant insights in determining how visual information from images plays a vital role in reflecting users' preferences in multimodal studies and in determining an ideal image processing method for the utmost recommendation performance. Third, this study utilizes a co-attention mechanism for modeling the complex interactions between text and images. Therefore, this study lays the theoretical foundation for the exploitation of multimodal data, providing contributions in the fields of multimodal learning as well as deep learning, and opening new possibilities for how to effectively fuse various data sources. The practical implications of this study are as follows. First, since the proposed CAMRec model can provide more sophisticated and personalized recommendations, users' experience will significantly improve. This may contribute to increasing customer satisfaction and user retention in e-commerce platforms. Second, the proposed CAMRec model is designed with domain-agnostic characteristics, making it highly scalable and adaptable across various domains. Although this study uses datasets from Amazon, which is common in recommender system studies, the model's architecture is flexible enough to be applied to other domains such as fashion, entertainment, or social media platforms. Since each domain involves multimodal data (e.g., texts and images), the CAMRec model can process data through its co-attention mechanism. Moreover, the scalability of the model supports large-scale datasets and growing operational demands, making it well-suited for providing efficient recommendations in contexts beyond e-commerce. Finally, the feature composition of our proposed CAMRec model is model-agnostic, which allows for flexible implementation. Therefore, businesses can easily apply it to their existing hardware or software environments without major overhauls. Such flexibility enables companies to overcome various technical constraints while optimizing the model for their specific operational needs.