  Predicting the popularity of online videos is important for video streaming
content providers. This is a challenging problem because of the following two
reasons. First, the problem is both "wide" and "deep". That is, it not only
depends on a wide range of features, but also be highly non-linear and complex.
Second, multiple competitors may be involved. In this paper, we propose a
general prediction model using the multi-task learning (MTL) module and the
relation network (RN) module, where MTL can reduce over-fitting and RN can
model the relations of multiple competitors. Experimental results show that our
proposed approach significantly increases the accuracy on predicting the total
view counts of TV series with RN and MTL modules.
