Application of iBeacon Locating Technology with Data Mining for Shopping Recommendation APP at Retailer Store

• Main Authors: Yong-Ren Li, 李永仁
• Other Authors: Kai-Ying Chen
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/gpf857

碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 103 === With advances in Big Data technology, retailers pay more attention to consumers’ information. Through data mining, system is able to recommend consumers’ favorite products. However, the current system can only analyze shopping records on POS and member cards, but it can’t analyze any shopping behavior. Recent years, Apple Company announced micro-positioning technology, iBeacon. With improved indoor positioning of iBeacon technology and high popularity of smart phones, which make retailers able to collect consumers’ position data. Via combination smart phone, position data and data mining, this study construct a system could send consumers the recommended list after analyzing consumers’ behavior on server immediately. The system could be helpful to supermarket on encouraging consumers purchase more. This study implemented the iBeacon technology in a test site with 9*9 square meters, to demonstrate how micro-positioning technology could support the supermarket to recommend products to consumers by the information of consumer’s position. First, the Received Signal Strength Indication (RSSI) is retrieved by smart phones. The reference points generated by Biquinary Notation then turn into consumers’ position, region and staying periods by LandMarc algorithms. Integrated with the market basket analysis, the additional position information enables the real-time recommendation service.