A weight-sharing Gaussian process model using web-based information for audience rating prediction

Abstract

In this paper, we describe a novel Gaussian process model for TV audience rating prediction. A weight-sharing covariance function well-suited for this problem is introduced. We extract several types of features from Google Trends and Facebook, and demonstrate that they can be useful in predicting the TV audience ratings. Experiments on a dataset consisting of daily dramas show that the proposed model outperforms the other conventional models given the same feature set.