Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89020
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Logistics and Maritime Studies | en_US |
dc.creator | Zhang, S | en_US |
dc.creator | Liao, P | en_US |
dc.creator | Ye, HQ | en_US |
dc.creator | Zhou, Z | en_US |
dc.date.accessioned | 2021-01-15T07:14:53Z | - |
dc.date.available | 2021-01-15T07:14:53Z | - |
dc.identifier.issn | 1742-6588 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/89020 | - |
dc.description | 3rd International Conference on Physics, Mathematics and Statistics, ICPMS 2020, 20-22 May 2020 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Physics Publishing | en_US |
dc.rights | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | en_US |
dc.rights | The following publication Siyu Zhang et al 2020 J. Phys.: Conf. Ser. 1592 012034 is available at https://dx.doi.org/10.1088/1742-6596/1592/1/012034 | en_US |
dc.title | Multiple resource allocation for precision marketing | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 15 | en_US |
dc.identifier.volume | 1592 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1088/1742-6596/1592/1/012034 | en_US |
dcterms.abstract | In the precision marketing of a new product, it is a challenge to allocate limited resources to the target customer groups with different characteristics. We presented a framework using distance-based algorithm, K-Nearest-Neighbour, and support vector machine to capture customers' preference towards promotion channel. Additionally, on-line learning programming was combined with machine learning strategies to fit a dynamic environment, evaluating its performance through a parsimonious model of minimum regret. A resource optimization model was proposed using classification results as input. In particular, we collected data from a loan agency that offers loans to small business merchants. Our sample contained 525,919 customers who will be introduced to a new financial product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected merchants when K-NN was performed with optimal resource allocation strategy. Our results also show that K-NN is the most stable method to perform classification, and that distance-based algorithm has the most efficient adoption with on-line learning. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of physics. Conference series, 2020, v. 1592, no. 1, 12034, p. 1-15 | en_US |
dcterms.isPartOf | Journal of physics. Conference series | en_US |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85092540109 | - |
dc.relation.conference | International Conference on Physics, Mathematics and Statistics [ICPMS] | en_US |
dc.identifier.eissn | 1742-6596 | en_US |
dc.identifier.artn | 12034 | en_US |
dc.description.validate | 202101 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Zhang_Multiple_Resource_Allocation.pdf | 1.15 MB | Adobe PDF | View/Open |
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