A multimodal guide for the augmented campus
Proceedings of the 35th annual ACM SIGUCCS fall conference
The sensor internet at work: Locating everyday items using mobile phones
Pervasive and Mobile Computing
Tagmark: reliable estimations of RFID tags for business processes
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
SOPHYA: a system for digital management of ordered physical document collections
Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction
Finding misplaced items in retail by clustering RFID data
Proceedings of the 13th International Conference on Extending Database Technology
Assessing and optimizing the range of UHF RFID to enable real-world pervasive computing applications
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Objects calling home: locating objects using mobile phones
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
FindingMiMo: tracing a missing mobile phone using daily observations
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
RFID-enabled shelf replenishment with backroom monitoring in retail stores
Decision Support Systems
Collaborative sensing in a retail store using synchronous distributed jam signalling
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Modeling trade-offs in the design of sensor-based event processing infrastructures
Information Systems Frontiers
Extracting users' interest from book-browsing behaviors recorded with RFID
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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Today a huge variety of methods to track and analyze the customers' behavior in e-commerce systems is available. However, in traditional retail stores such systems are not widely known and therefore the customers' behavior is considered as a black box in this domain. This paper presents the Smart Shelf technology able to track basic simple actions, such as take, return and remove, which are performed on items by the customer. These actions form the interaction context replacing the black box. We will show that this context can be used to enhance existing data mining and store management systems as well as the customer will benefit from recommendation systems comparable to those used in ecommerce systems of online stores.