Social User Profiling for Personalized Advertisement Recommendation A Multi-Modal Deep Learning Approach

The exponential growth of social media platforms has created unprecedented opportunities for targeted advertising, yet existing recommendation systems often struggle with user engagement and retention. This paper presents a novel multi-modal deep learning framework for social user profiling that combines computer vision and natural language processing techniques to enhance advertisement recommendation systems. Our approach integrates Convolutional Neural Networks (CNNs) for visual content analysis with advanced NLP models, including TunBERT for Arabic dialect processing, to create comprehensive user profiles. The system employs web scraping techniques to gather social media data and utilizes both Firebase and MongoDB for scalable data management. Through the integration of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), our framework demonstrates improved user profiling accuracy compared to traditional demographic-based approaches. The proposed system addresses the critical gap in recommendation systems for Arabic-speaking markets, particularly in North Africa, where cultural and linguistic nuances significantly impact user preferences. Our implementation shows promising results in creating more personalized and effective advertisement targeting strategies.