User Conditional Hashtag Recommendation for Micro-videos

When a user tend to publish a micro-video, hashtag recommendation aims to suggest hashtags that can reflect the theme or contents of the micro-video, and meet the user tagging preference as well. In this paper, we show how user profile and historical hashtags combined with micro-video representations can be used to perform hashtag recommendation. Specifically, a User-guided Hierarchical Multi-head Attention Network (UHMAN) is proposed to attend both image-level and video-level representations of micro-videos with user side information. We evaluate the proposed model on the dataset collected from micro-video sharing platform Musical.ly. The experimental results demonstrate the effectiveness of the proposed method.