Hierarchical Attention-Based Model for Social Contextual Image Recommendations

This study proposes a Hierarchical Attention Model (HAM) that combines attention mechanisms with Convolutional Neural Networks (CNNs) to select social contextual images. Each user's essential content is prioritized by HAM, which learns visual attributes and social context on two levels. The program uses social context to give highly tailored visuals and dynamically modifies recommendations based on user interactions. Contextually relevant recommendations can improve user engagement on social media platforms, where HAM can be customized to provide personalized images in a scalable and effective manner.