Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105674
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Chen, Y | en_US |
dc.creator | Zhang, J | en_US |
dc.creator | Guo, M | en_US |
dc.creator | Cao, J | en_US |
dc.date.accessioned | 2024-04-15T07:35:49Z | - |
dc.date.available | 2024-04-15T07:35:49Z | - |
dc.identifier.isbn | 978-1-5090-4338-5 (Electronic) | en_US |
dc.identifier.isbn | 978-1-5090-4339-2 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105674 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Yuanyi Chen, Jinyu Zhang, Minyi Guo and Jiannong Cao, "Understanding customer behaviour in urban shopping mall from WiFi logs," 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, 2017, pp. 50-53 is available at https://doi.org/10.1109/PERCOMW.2017.7917519. | en_US |
dc.title | Understanding customer behaviour in urban shopping mall from WiFi logs | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 50 | en_US |
dc.identifier.epage | 53 | en_US |
dc.identifier.doi | 10.1109/PERCOMW.2017.7917519 | en_US |
dcterms.abstract | Traditional ways of understanding customer behaviour are mainly based on predominantly field surveys, which are not effective as they require labor-intensive survey. As mobile devices and ubiquitous sensing technologies are becoming more and more pervasive, user-generated data from these platforms are providing rich information to uncover customer preference. In this study, we propose a shop recommendation model for urban shopping mall by exploiting user-generated WiFi logs to learn customer preference. Specifically, the proposed model consists of two phases: 1) offline learning customer's preference from their check-in activities; 2) online recommendation by fusing the learnt preference and temporal influence. We have performed a comprehensive experiment evaluation on a real dataset collected by over 39,000 customers during 7 months, and the experiment results show the proposed recommendation model outperforms state-of-the-art methods. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 13-17 March 2017, Big Island, HI, USA, p. 50-53 | en_US |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85020028823 | - |
dc.relation.conference | IEEE Annual Conference on Pervasive Computing and Communications Workshops [PerCom] | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-1259 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Basic Research 973 Program of China; Program for National Natural Science Foundation of China / Research Grants Council (NSFC/RGC)(612191030; Scientific Innovation Act of STCSM; EU FP7 CLIMBER project | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9596870 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Cao_Understanding_Customer_Behaviour.pdf | Pre-Published version | 852.84 kB | Adobe PDF | View/Open |
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