A Multimodal Deep Learning Framework for Personalized Product Recommendation Using User Behavioral Modeling

The fast growing online platforms has increased the challenge of excessive information that make personalized recommendation systems an important element. Conventional collaborative filtering and content-based approaches were effective, but they remain limited in handling sparse data and heterogeneous item. Recent developments in deep learning have started use of integration of multiple data modalities, including user behavior signals, visual features, and textual descriptions in unified recommendation frameworks. This review paper provides overall analysis of multimodal deep learning techniques for personalized recommendation systems. The evolution from matrix factorization to neural collaborative filtering, transformer-based text modeling, convolutional visual encoding, and attention-based fusion strategies is examined. In addition, ranking objectives, optimization techniques, cross-modal alignment, a representative end to end pipeline presented with a working blueprint interface, and open research challenges are discussed.