articleOct 12, 2013Closed access

Hidden factors and hidden topics

Stanford University · Stanford Medicine

Indexed incrossref

Abstract

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 the form of a numeric rating accompanied by review text. However, traditional methods often discard review text, which makes user and product latent dimensions difficult to interpret, since they ignore the very text that…

Citation impact

1,590
total citations
FWCI
134.40
Percentile
100%
References
38
Citations per year

Authors

2

Topics & keywords

Keywords
  • Computer science
  • Product (mathematics)
  • Recommender system
  • Order (exchange)
  • Information retrieval
  • Topic model
  • Data science
  • Latent variable
No related works found for this paper.

Funding