YouTube thumbnail design recommendation systems using image-tabular multimodal data for Thai’s YouTube thumbnail

This study analyzes YouTube thumbnails to identify key elements that distinguish different categories and attract viewers, specifically focusing on YouTubers in Thailand. Using a fine-tuned Convolutional Neural Network model named Xception, we classified images into food, IT, and travel categories with 88% accuracy. Object detection models identified visual objects in the thumbnails, and the combined classification and detection results were clustered into three groups using K-means. Analysis of each cluster led to category-specific recommendations for food, IT, and travel images. While our novel method of combining multimodal tabular and image features is applicable to various regions, it requires region-specific training data for optimal performance in object detection. Nonetheless, our work is the first to offer concrete advice to novice creators on generating interest through YouTube thumbnails across different composition aspects compared to successful YouTubers.