Revolutionizing Image Recommendations: A Novel Approach with Social Context and CNN

All social networks allow image uploads and sharing. To allow users to exchange photographs, social networking sites use content-based recommendations (based on history), collaborative suggestions (based on the user and his friends' similarities), personalized advice, etc. Since no previous technique used socially advanced qualities like Upload History, Social Influence, or Owner Admiration, we can acquire a context relationship between people and images, which helps make optimal relationship-based suggestions. This generates a hierarchical attention model with three essential aspects and a CNN, where CNN represents the User's visual image model and three critical aspects reflect upload history, social influence, and owner matrix. The proposed application improved accuracy, sensitivity, and recall to 93.23%, 95.23%, and 97.73 %, respectively.