Co-attention Memory Network for Multimodal Microblog's Hashtag Recommendation

Abstract

Hashtags are keywords describing a topic or a theme and are usually chosen by microblogging users. Hence, the hashtags can be used to categorize microblog posts. With the fast development of the social network, the task of recommending suitable hashtags has received considerable attention in recent years. Recently, most neural network methods have treated the task as a multi-class classification problem. In fact, users are constantly introducing new hashtags in a highly dynamic way. Treating the task as a multi-class classification problem with a fixed number of target categories does not allow the method to deal with the new hashtags. To address this problem, the task is reinterpreted as a matching problem and a novel co-attention memory network is proposed to represent the multimodal microblogs and hashtags. We utilize a co-attention mechanism to model the multimodal mircroblogs, and utilize the post history to represent the hashtags. Experimental results on a Twitter-based dataset demonstrated that the proposed method can achieve better performance than the current state-of-the-art methods that treat the task as a multi-class classification problem.