Open-Domain Trending Hashtag Recommendation for Videos

Swapneel Mehta, Somdeb Sarkhel, Xiang Chen, Saayan Mitra, Viswanathan Swaminathan, Ryan A. Rossi, Ali Aminian, Han Guo, Kshitiz Garg

Abstract: We describe a novel algorithm for an open-domain trending hashtag recommendation task using zero-shot hashtag prediction in an online learning paradigm. Our method utilizes joint representation learning of latent embeddings for features extracted from long-form videos and semantic embeddings of hashtags trending on social platforms. In particular, we apply graph convolutional networks to a link prediction task using videos and hashtags as nodes in a heterogeneous graph. Comparing it to the existing models for closely related tasks, we demonstrate state-of-the-art results in trending hashtag recommendations for videos. The architecture is designed to be modular in a plug-and-play fashion to enable quick and easy incorporation of the latest advances in natural language understanding and image and video processing, with a practical view to its implementation in a real-time, online setting.