@InProceedings{ni-EtAl:2017:I17-1,
  author    = {Ni, Jianmo  and  Lipton, Zachary C.  and  Vikram, Sharad  and  McAuley, Julian},
  title     = {Estimating Reactions and Recommending Products with Generative Models of Reviews},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {783--791},
  abstract  = {Traditional approaches to recommendation focus on learning from large volumes
	of historical feedback to estimate simple numerical quantities (Will a user
	click on a product? Make a purchase? etc.). Natural language approaches that
	model information like product reviews have proved to be incredibly useful in
	improving the performance of such methods, as reviews provide valuable
	auxiliary information that can be used to better estimate latent user
	preferences and item properties.
	In this paper, rather than using reviews as an inputs to a recommender system,
	we focus on generating reviews as the model's output. This requires us to
	efficiently model text (at the character level) to capture the preferences of
	the user, the properties of the item being consumed, and the interaction
	between them (i.e., the user's preference). We show that this can model can be
	used to (a) generate plausible reviews and estimate nuanced reactions; (b)
	provide personalized rankings of existing reviews; and (c) recommend existing
	products more effectively.},
  url       = {http://www.aclweb.org/anthology/I17-1079}
}