@InProceedings{chen-EtAl:2016:COLING2,
  author    = {Chen, Wenliang  and  Zhang, Zhenjie  and  Li, Zhenghua  and  Zhang, Min},
  title     = {Distributed Representations for Building Profiles of Users and Items from Text Reviews},
  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     = {2143--2153},
  abstract  = {In this paper, we propose an approach to learn distributed representations of
	users and items from text comments for recommendation systems. Traditional
	recommendation algorithms, e.g. collaborative filtering and matrix completion,
	are not designed to exploit the key information hidden in the text comments,
	while existing opinion mining methods do not provide direct support to
	recommendation systems with useful features on users and items. Our approach
	attempts to construct vectors to represent profiles of users and items under a
	unified framework to maximize word appearance likelihood. Then, the vector
	representations are used for a recommendation task in which we predict scores
	on unobserved user-item pairs without given texts. The recommendation-aware
	distributed representation approach is fully supported by effective and
	efficient learning algorithms over massive text archive. Our empirical
	evaluations on real datasets show that our system outperforms the
	state-of-the-art baseline systems.},
  url       = {http://aclweb.org/anthology/C16-1202}
}