Visual Background Recommendation for Dance Performances Using Deep Matrix Factorization

The stage background is one of the most important features for a dance performance, as it helps to create the scene and atmosphere. In conventional dance performances, the background images are usually selected or designed by professional stage designers according to the theme and the style of the dance. In new media dance performances, the stage effects are usually generated by media editing software. Selecting or producing a dance background is quite challenging and is generally carried out by skilled technicians. The goal of the research reported in this article is to ease this process. Instead of searching for background images from the sea of available resources, dancers are recommended images that they are more likely to use. This work proposes the idea of a novel system to recommend images based on content-based social computing. The core part of the system is a probabilistic prediction model to predict a dancer’s interests in candidate images through social platforms. Different from traditional collaborative filtering or content-based models, the model proposed here effectively combines a dancer’s social behaviors (rating action, click action, etc.) with the visual content of images shared by the dancer using deep matrix factorization (DMF). With the help of such a system, dancers can select from the recommended images and set them as the backgrounds of their dance performances through a media editor. According to the experiment results, the proposed DMF model outperforms the previous methods, and when the dataset is very sparse, the proposed DMF model shows more significant results.