Title: Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service,
Abstract: In the industry of video content providers such as VOD and IPTV, predicting
the popularity of video contents in advance is critical not only from a
marketing perspective but also from a network optimization perspective. By
predicting whether the content will be successful or not in advance, the
content file, which is large, is efficiently deployed in the proper service
providing server, leading to network cost optimization. Many previous studies
have done view count prediction research to do this. However, the studies have
been making predictions based on historical view count data from users. In this
case, the contents had been published to the users and already deployed on a
service server. These approaches make possible to efficiently deploy a content
already published but are impossible to use for a content that is not be
published. To address the problems, this research proposes a hybrid machine
learning approach to the classification model for the popularity prediction of
newly video contents which is not published. In this paper, we create a new
variable based on the related content of the specific content and divide entire
dataset by the characteristics of the contents. Next, the prediction is
performed using XGBoosting and deep neural net based model according to the
data characteristics of the cluster. Our model uses metadata for contents for
prediction, so we use categorical embedding techniques to solve the sparsity of
categorical variables and make them learn efficiently for the deep neural net
model. As well, we use the FTRL-proximal algorithm to solve the problem of the
view-count volatility of video content. We achieve overall better performance
than the previous standalone method with a dataset from one of the top
streaming service company.