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UID:pretalx-cfp-GLQQHY@seattle2023.pydata.org
DTSTART;TZID=America/Los_Angeles:20230428T110000
DTEND;TZID=America/Los_Angeles:20230428T114500
DESCRIPTION:Case study that describes how a scrappy science and engineering
  team built an optimal recommendations engine for consumer banking and Fin
 Tech mobile app users.  The engine produces high-response\, tailored end-u
 ser results from anonymized and incomplete data\, the application of quant
 um particle swarm optimization techniques\, and by leveraging a homegrown 
 knowledge representation graph.
DTSTAMP:20250709T220105Z
LOCATION:Hood
SUMMARY:You Want to Buy This - Particle Swarm Classification for Next-Gen R
 ecommendation Engines - Eugene Ciurana
URL:https://seattle2023.pydata.org/cfp/talk/GLQQHY/
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