  Existing micro-video recommendation models exploit the interactions between
users and micro-videos and/or multi-modal information of micro-videos to
predict the next micro-video a user will watch, ignoring the information
related to vloggers, i.e., the producers of micro-videos. However, in
micro-video scenarios, vloggers play a significant role in user-video
interactions, since vloggers generally focus on specific topics and users tend
to follow the vloggers they are interested in. Therefore, in the paper, we
propose a vlogger-augmented graph neural network model VA-GNN, which takes the
effect of vloggers into consideration. Specifically, we construct a tripartite
graph with users, micro-videos, and vloggers as nodes, capturing user
preferences from different views, i.e., the video-view and the vlogger-view.
Moreover, we conduct cross-view contrastive learning to keep the consistency
between node embeddings from the two different views. Besides, when predicting
the next user-video interaction, we adaptively combine the user preferences for
a video itself and its vlogger. We conduct extensive experiments on two
real-world datasets. The experimental results show that VA-GNN outperforms
multiple existing GNN-based recommendation models.
