Mobile computing in the retail arena
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting customer shopping lists from point-of-sale purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent Guidance and Suggestions Using Case-Based Planning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Predictive text input in a mobile shopping assistant: methods and interface design
Proceedings of the 14th international conference on Intelligent user interfaces
Supporting the supermarket shopping experience through a context-aware shopping trolley
OZCHI '09 Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group: Design: Open 24/7
MUCS: A model for ubiquitous commerce support
Electronic Commerce Research and Applications
A framework for context-aware digital signage
AMT'11 Proceedings of the 7th international conference on Active media technology
Measuring performance in the retail industry (position paper)
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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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.