  Interactions between users and videos are the major data source of performing
video recommendation. Despite lots of existing recommendation methods, user
behaviors on videos, which imply the complex relations between users and
videos, are still far from being fully explored. In the paper, we present a
model named Sagittarius. Sagittarius adopts a graph convolutional neural
network to capture the influence between users and videos. In particular,
Sagittarius differentiates between different user behaviors by weighting and
fuses the semantics of user behaviors into the embeddings of users and videos.
Moreover, Sagittarius combines multiple optimization objectives to learn user
and video embeddings and then achieves the video recommendation by the learned
user and video embeddings. The experimental results on multiple datasets show
that Sagittarius outperforms several state-of-the-art models in terms of
recall, unique recall and NDCG.
