@InProceedings{huang-EtAl:2016:COLING,
  author    = {Huang, Haoran  and  Zhang, Qi  and  Gong, Yeyun  and  Huang, Xuanjing},
  title     = {Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {943--952},
  abstract  = {On microblogging services, people usually use hashtags to mark microblogs,
	which have a specific theme or content, making them easier for users to find.
	Hence, how to automatically recommend hashtags for microblogs has received much
	attention in recent years. Previous deep neural network-based hashtag
	recommendation approaches converted the task into a multi-class classification
	problem. However, most of these methods only took the microblog itself into
	consideration.              Motivated by the intuition that the history of users
	should
	impact the recommendation procedure, in this work, we extend end-to-end memory
	networks to perform this task. We incorporate the histories of users into the
	external memory and introduce a hierarchical attention mechanism to select more
	appropriate histories. To train and evaluate the proposed method, we also
	construct a dataset based on microblogs collected from Twitter. Experimental
	results demonstrate that the proposed methods can significantly outperform
	state-of-the-art methods. By incorporating the hierarchical attention
	mechanism, the relative improvement in the proposed method over the
	state-of-the-art method is around 67.9\% in the F1-score.},
  url       = {http://aclweb.org/anthology/C16-1090}
}