【摘要】 近年来,Hashtag推荐任务吸引了很多研究者的关注。目前,大部分深度学习方法把这个任务看作是一个多标签分类问题,将Hashtag看作为微博的类别。但是这些方法的输出空间固定,在没有进行重新训练的情况下,不能处理训练不可见的Hashtag。然而,实际上Hashtag会随着时事热点不断快速更新。为了解决这一问题,该文提出将Hashtag推荐任务建模成小样本学习任务。同时,结合用户使用Hashtag的偏好降低推荐的复杂度。在真实的推特数据集上的实验表明,与目前最优方法相比,该模型不仅可以取得更好的推荐结果,而且表现得更为鲁棒。更多还原

【Abstract】 Hashtag recommendation has received considerable attention in recent years. Most existing deep learning methods formulate this task as a multi-class classification problem to categorize tweets into a fixed number of target classes. However, as new hashtags are continuously introduced by users with daily bursts of news, these methods fail to tackle new hashtags without retraining. To solve this problem, we proposed to convert hashtag recommendation task to a few-shot learning problem. In addition, we combined users’ preference for hashtag usage to reduce the complexity of recommendation algorithm. Experimental results on the real-world dataset demonstrate that our method achieves significant performance improvement over the state-of-the-art methods and is more robust.更多还原