Deep Learning-Based System for Individualized Recommendations

In order to suggest people with appropriate goods or content, recommendation systems are widely employed across a variety of industries, including e-commerce, social networks, and entertainment. These systems aim to improve user experience and engagement by leveraging data-driven techniques to generate personalized recommendations. The recommended method provides user-item interaction by utilizing a variety of inputs, such as information from social media and Internet log data. This user-item interaction is subsequently provided to a graph neural network to improve accuracy. The suggested items are then created by giving this to the matrix factorization. Moreover, we discuss issues including data scarcity, cold start, and diversity. The given model has precision 0.91, recall 0.83, and F-measure 0.86 which show better results than baseline models.