Design and Implementation of an RFID-Based Customer Shopping Behavior Mining System - 2017


Shopping behavior knowledge is of nice importance in understanding the effectiveness of promoting and merchandising campaigns. Online clothes shops are capable of capturing client shopping behavior by analyzing the clicking streams and client searching carts. Retailers with physical clothing stores, but, still lack effective methods to comprehensively establish shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags will be exploited to detect and record how customers browse stores, that clothes they concentrate to, and that garments they sometimes combine up. The intuition is that the phase readings of tags connected to items can demonstrate distinct nonetheless stable patterns in a time-series when customers study, pick out, or turn over desired items. We have a tendency to design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of your time-series section readings to detect comprehensive looking behaviors. We have a tendency to have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from 2-week shopping-like data show that ShopMiner is able to identify customer searching behaviors with high accuracy and low overhead, and is robust to interference.

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