Hidden factors and hidden topics: understanding rating dimensions with review text

@article{McAuley2013HiddenFA,
  title={Hidden factors and hidden topics: understanding rating dimensions with review text},
  author={Julian McAuley and J. Leskovec},
  journal={Proceedings of the 7th ACM conference on Recommender systems},
  year={2013}
}
In order to recommend products to users we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to identify that the book is about wizards, as well as the user's level of interest in wizardry. User feedback is required to discover these latent product and user dimensions. Such feedback often comes in… Expand
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