Zero-shot Micro-video Classification with Neural Variational Inference in Graph Prototype Network

Micro-video classification plays a central role in online content recommendation platforms, such as Kwai and Tik-Tok. Existing works on video classification largely exploit the interactions between users and items as well as the item labels to provide quality recommendation services. However, scarce or even no labeled data of emerging videos is a great challenge for existing classification methods. In this paper, we propose a zero-shot micro-video classification model (NVIGPN) by exploiting the hidden topics behind items to guide the representation learning in user-item interactions. Specifically, we study this zero-shot classification in two stages: (1) exploiting a generalized semantic hidden topic descriptions for transferable knowledge learning, and (2) designing a graph-based learning model for guiding the minor seen class information to the unseen ones. Through mining the transferable knowledge between the hidden topics and the small number of the seen classes, NVIGPN can achieves state-of-the-art performances in predicting the unseen classes of micro-videos. We conduct extensive experiments to demonstrate the effectiveness of our method.