Mention Recommendation for Multimodal Microblog with Cross-attention Memory Network.

The users of Twitter-like social media normally use the "@'' sign to select a suitable person to mention. It is a significant role in promoting the user experience and information propagation. To help users easily find the usernames they want to mention, the mention recommendation task has received considerable attention in recent years. Previous methods only incorporated textual information when performing this task. However, many users not only post texts on social media but also the corresponding images. These images can provide additional information that is not included in the text, which could be helpful in improving the accuracy of a mention recommendation. To make full use of textual and visual information, we propose a novel cross-attention memory network to perform the mention recommendation task for multimodal tweets. We incorporate the interests of users with external memory and use the cross-attention mechanism to extract both textual and visual information. Experimental results on a dataset collected from Twitter demonstrated that the proposed method can achieve better performance than state-of-the-art methods that use textual information only.