A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering

Abstract

Social media platforms generate a large amount of content users create, which requires methods for suggesting relevant content. In current empirical research introduces an approach to improving social content recommendations using the Deep Self Organizing Map (DSOM) algorithm and hierarchical clustering. The study uses a database that includes user posts, comments, likes, shared content, and user profiles. The DSOM algorithm analyzes and organizes the data, while hierarchical clustering enhances performance. By utilizing the insights gathered from this social content database, we can significantly improve the accuracy and relevance of recommendations. This improvement will ultimately increase user engagement and satisfaction on social media platforms. The findings of this research have implications for recommendation systems on social media platforms and strategies related to promoting content and analyzing user behavior.