TSCREC: Time-sync Comment Recommendation in Danmu-Enabled Videos

In recent years, Time-sync Comment (TSC), as known as “Danmaku” or “Danmu”, has been increasingly recognized as a valuable representation of video being incorporated into the process of generating video or highlight recommendation. However, little work has studied how to recommend proper TSC when users are watching the video. Providing some suggestions for users when they want to post TSCs can not only enhance the real-time interactions among users within videos, but also motivate users to post more TSCs which could be useful for video or highlight recommendation in return. In order to accomplish the TSC recommendation task, we extract candidates from existing comments sent by other users. Specifically, we propose a model, namely “TSCREC”, which uses a bidirectional Gated Recurrent Unit to capture the semantic meaning of existing comments and assigns scores for comments by a Multi-Layer Perceptron. Furthermore, considering the importance of correlations among comments, we also take similarities among comments as features. In addition, we design a set of novel evaluation metrics which combines n-grams overlapping and ranking order to measure the quality of the recommendation list. We conduct experiments on realworld datasets, and the results show that our TSCREC model outperforms baseline approaches.