Building intelligent shopping assistants using individual consumer models

Chad Cumby*, Andrew Fano, Rayid Ghani, Marko Krema

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

23 Scopus citations


This paper describes an Intelligent Shopping Assistant designed for a shopping cart mounted tablet PC that enables individual interactions with customers. We use machine learning algorithms to predict a shopping list for the customer's current trip and present this list on the device. As they navigate through the store, personalized promotions are presented using consumer models derived from loyalty card data for each inidvidual. In order for shopping assistant devices to be effective, we believe that they have to be powered by algorithms that are tuned for individual customers and can make accurate predictions about an individual's actions. We formally frame the shopping list prediction as a classification problem, describe the algorithms and methodology behind our system, and show that shopping list prediction can be done with high levels of accuracy, precision, and recall. Beyond the prediction of shopping lists we briefly introduce other aspects of the shopping assistant project, such as the use of consumer models to select appropriate promotional tactics, and the development of promotion planning simulation tools to enable retailers to plan personalized promotions delivered through such a shopping assistant.

Original languageEnglish (US)
Number of pages3
StatePublished - 2005
Externally publishedYes
EventIUI 05 - 2005 International Conference on Intelligent User Interfaces - San Diego, CA, United States
Duration: Jan 9 2005Jan 12 2005


OtherIUI 05 - 2005 International Conference on Intelligent User Interfaces
Country/TerritoryUnited States
CitySan Diego, CA


  • Classification
  • Machine learning
  • Retail applications

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction


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