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Personalized Recommendations, Context-Aware, Multi-Modal Deep Learning, Retail, User Experience.

Increased need for personalized shopping experience has made high-end recommendation systems a part of standard e-commerce platforms today. Basic methods such as content-based filtering and collaborative filtering simply fail to keep pace with the dynamics of user needs, particularly under dynamic retailing scenarios. Herein, in this paper, a context-aware multi-modal deep learning architecture involving text, image, and context information sources is proposed to facilitate enhanced recommendation relevance and accuracy. Using convolutional neural networks (CNNs) for visual processing, transformer-based language models for natural language processing, and real-time contextual embeddings, our system learns highly complex user-product associations in an optimal manner. Python machine learning frameworks TensorFlow, PyTorch, and Scikit-learn are used for model deployment, while Apache Spark is used for handling big data. Experimental results show that our method far surpasses baseline recommendation models with better accuracy, diversity, and engagement. Public retail datasets are marked with performance tests for 15% more accurate and 20% more recall than baseline models. Cold-start problems are also efficiently dealt with by our system using multi-modal data sources to provide good recommendations for new users and new products. Computational complexity is a problem, but optimization methods such as model pruning and efficient data fusion algorithms can make it scalable. This research underscores the potential of the synergy between multi-modal and contextual data processing and deep learning in creating highly personal and adaptive recommendation models with profound implications for future retail deployment of AI.