@InProceedings{xing-paul:2017:WNUT,
  author    = {Xing, Linzi  and  Paul, Michael J.},
  title     = {Incorporating Metadata into Content-Based User Embeddings},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {45--49},
  abstract  = {Low-dimensional vector representations of social media users can benefit
	applications like recommendation systems and user attribute inference. Recent
	work has shown that user embeddings can be improved by combining different
	types of information, such as text and network data. We propose a data
	augmentation method that allows novel feature types to be used within
	off-the-shelf embedding models. Experimenting with the task of friend
	recommendation on a dataset of 5,019 Twitter users, we show that our approach
	can lead to substantial performance gains with the simple addition of network
	and geographic features.},
  url       = {http://www.aclweb.org/anthology/W17-4406}
}