Distribution-based aggregation for relational learning with identifier attributes

@article{Perlich2006DistributionbasedAF,
  title={Distribution-based aggregation for relational learning with identifier attributes},
  author={Claudia Perlich and Foster J. Provost},
  journal={Machine Learning},
  year={2006},
  volume={62},
  pages={65-105}
}
Identifier attributes—very high-dimensional categorical attributes such as particular product ids or people's names—rarely are incorporated in statistical modeling. However, they can play an important role in relational modeling: it may be informative to have communicated with a particular set of people or to have purchased a particular set of products. A key limitation of existing relational modeling techniques is how they aggregate bags (multisets) of values from related entities. The… CONTINUE READING
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