Digital Media Visual Recommendation System Based on Artificial Neural Network Machine Learning

Abstract

Conventional collaborative filtering methods struggle to understand the subtle connections between users and evolving short video content, often resulting in imprecise or broad recommendations. Additionally, the absence of interpretability, usually displayed as unchanging lists or tables, restricts user confidence and involvement. To overcome these challenges, this research suggests a tailored short video recommendation system that integrates a CNN-BiLSTM hybrid model with a Multi-Head Attention (MHA) approach and visualization techniques. CNN is utilized to obtain visual characteristics from brief videos, BiLSTM identifies temporal relationships in video sequences and user actions, while MHA improves feature weighting for tailored significance. To address the transparency concern, the system incorporates real-time visualization methods like heat maps and interactive charts, enabling users to grasp the reasoning behind each suggestion. Experimental findings from the MicroLens dataset indicate that the proposed model achieves a hit rate of 0.94 at k = 15, outperforming conventional methods such as ItemCF by 0.16. This method greatly enhances the accuracy of recommendations, transparency, and user engagement in digital media contexts.