Validating Self-reported Trends Using WiFi Tracking

被引：0

作者：

Ebeling, Daniel ^{[1
]}

Luker, Zach ^{[1
]}

Pacheco, Seth ^{[1
]}

Payne, Angela ^{[1
]}

Rae, Nikki ^{[1
]}

机构：

[1] Brigham Young Univ, Informat Technol, Provo, UT 84602 USA

来源：

HCI INTERNATIONAL 2018 - POSTERS' EXTENDED ABSTRACTS, PT III | 2018年 / 852卷

关键词：

Wi-Fi tracking; Machine learning; Customer Experience; Location-based tracking; Time-based tracking; Probe requests; Observational tracking;

D O I：

10.1007/978-3-319-92285-0_32

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Observational data gathering is expensive as it traditionally relies on human intervention and intuition. However, with advances in Artificial Intelligence (AI) machines are gaining the capacity in making sense of the unstructured data observational methods yield [1]. With these advances in technology there is a new push to create innovative ways of gathering observational data to give context to self-reported user feedback.

引用

页码：233 / 237

页数：5

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