Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Engineering | en_US |
| dc.contributor | Department of Computing | en_US |
| dc.contributor.advisor | Chan, C. C. Keith (COMP) | en_US |
| dc.creator | Yang, Fan | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/10673 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Consumer behavior analysis based on smartphone sensory data using mobile augmented reality framework | en_US |
| dcterms.abstract | Consumer behavior analysis (CBA) is concerned with understanding how consumer cognitive processes influence consumer decision making, choices, and behavior which plays an important role in intelligent retail system (IRS). The complexity of off-line CBA arises from many challenges such as insufficient features of consumer personas. In today's "new retail" economy, innovative solutions of smart technologies in retail settings has started catching the attention of scholars and practitioners. In particular, the usage of smartphone sensory data within augmented reality (AR) technology allows retailers to provide superior consumers' experiences and achieve increased business profitability. Aims to infer consumer preference in retailing, we study on sensing retail context and present a context-aware model called UTLIP to execute the CBA process. Base on the CBA engine, we develop an innovative system named "Easy Buy" by combining the latest mobile technologies with AR framework to capture personas labels in real-time and use the system to analyze consumer behavior in retail environments. To achieve the precise personalized recommendation goal on "Easy Buy", three key technologies are implemented: indoor positioning, mobile AR implementing and consumer behavior modeling. Preliminary evaluation indicates that the precision of indoor localization of our approach is less than 0.2 meter on average in corridor environments. Test results also indicate that the proposed scene mapping RP matching rates is over 80% and average error distances for pose estimation is within 1 meter in our mobile AR framework. Finally, we have successfully achieved the accurate personalized recommendation result via UTLIP model with a recognition accuracy rate of 91.5%. | en_US |
| dcterms.extent | xiv, 119 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2020 | en_US |
| dcterms.educationalLevel | Eng.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Consumer behavior | en_US |
| dcterms.LCSH | Smartphones -- Social aspects | en_US |
| dcterms.LCSH | Retail trade -- Management | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5111.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.62 MB | Adobe PDF | View/Open |
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