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Regular version of the site

Book chapter

Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences

P. 3-11.
Frolov D., Mirkin B., Nascimento S., Fenner T., Taran Z.

We define a most specific generalization of a fuzzy set of
topics assigned to leaves of the rooted tree of a domain taxonomy. This
generalization lifts the set to its “head subject” node in the higher ranks
of the taxonomy tree. The head subject is supposed to “tightly” cover
the query set, possibly bringing in some errors referred to as “gaps” and
“offshoots”. Our method, ParGenFS, globally minimizes a penalty function
combining the numbers of head subjects and gaps and offshoots,
differently weighted. Two applications are considered: (1) analysis of
tendencies of research in Data Science; (2) audience extending for programmatic
targeted advertising online. The former involves a taxonomy
of Data Science derived from the celebrated ACM Computing Classification
System 2012. Based on a collection of research papers published
by Springer 1998–2017, and applying in-house methods for text analysis

retrieval and clustering. The head subjects of these clusters inform us
of some general tendencies of the research. The latter involves publicly
available IAB Tech Lab Content Taxonomy. Each of about 25 mln users
is assigned with a fuzzy profile within this taxonomy, which is generalized
offline using ParGenFS. Our experiments show that these head subjects
effectively extend the size of targeted audiences at least twice without
loosing quality.
 




















In book

Edited by: H. Yin. Vol. 2. Springer, 2019.