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Real-time smartphone sensing and recommendationstowards context-awareness shopping

By Chia-Chen Chen, Tien-Chi Huang, James J. Park and Neil Y. Yen


This study investigates a smart environment,namely ‘‘Intelligent Shopping-aid Sensing System (iS3)’’for online shopping support in the next era by developing acontext-aware automated service system. Sensors, radiofrequency identification (RFID), are applied for the recognition,collection, and delivery of user contexts. Followingthe collected contexts from sensors, integratedmining and analysis techniques (i.e., customized clusteringanalysis and association rules) were implemented for theprovision of instant and personal information to users.Information of products, such as locations, specifications,and characteristics can be collected quickly through thedeployed RFID reader and display. Moreover, localapplications on mobile devices offer real-time interactionsbetween central system and end users. The system isexpected to prompt the product promotion, inquiry andonline marketing to shopping malls (and related companiesas well). In the empirical results, the quality of recommendationswith the proposed approach reaches 70 %accuracy rate. The traditional and non-clustering approachesare 56 and 46 %, respectively. This study reduceslong-term operation costs of retailers, stimulates serviceinnovation and experience economy and enhances corporateoperational performance

Topics: Smartphone sensing, Recommendation, system, Context-awareness, Association rules
Year: 2014
DOI identifier: 10.1007/s00530-013-0348-7
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