Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/ESBF-9GMN7J
Full metadata record
DC Field | Value | Language |
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dc.contributor.advisor1 | Nivio Ziviani | pt_BR |
dc.contributor.advisor-co1 | Adriano Alonso Veloso | pt_BR |
dc.contributor.referee1 | Berthier Ribeiro de Araujo Neto | pt_BR |
dc.contributor.referee2 | Leandro Balby Marinho | pt_BR |
dc.contributor.referee3 | Ricardo Baeza-yates | pt_BR |
dc.contributor.referee4 | Wagner Meira Junior | pt_BR |
dc.creator | Anisio Mendes Lacerda | pt_BR |
dc.date.accessioned | 2019-08-09T23:17:59Z | - |
dc.date.available | 2019-08-09T23:17:59Z | - |
dc.date.issued | 2013-12-20 | pt_BR |
dc.identifier.uri | http://hdl.handle.net/1843/ESBF-9GMN7J | - |
dc.description.abstract | Daily-deals sites (DDSs), such as Groupon and Peixe Urbano, attract millions of customers in the hunt for offers at significantly reduced prices. The challenge of DDSs is to find the best match between deals and customers while generating as much revenue as possible. One important objective of a DDS is to improve the aggregated value customers give to emails, which should not be seen as spam. This thesis solves three different problems in order to guarantee revenue maximization and customer satisfaction. First, a method for predicting the number of coupons a deal is going to sell is proposed. Second, we present an email prioritization approach. Third, we introduce a new strategy for deals recommendation via email. All three methods improved the results of state-of-the-art algorithms for the tasks being addressed, with gains in precision varying from 7% to 21%, while reducing the number of emails sent in 40% without affecting the number of customers clicking the deals in emails. | pt_BR |
dc.description.resumo | Daily-deals sites (DDSs), such as Groupon and Peixe Urbano, attract millions of customers in the hunt for offers at significantly reduced prices. The challenge of DDSs is to find the best match between deals and customers while generating as much revenue as possible. One important objective of a DDS is to improve the aggregated value customers give to emails, which should not be seen as spam. This thesis solves three different problems in order to guarantee revenue maximization and customer satisfaction. First, a method for predicting the number of coupons a deal is going to sell is proposed. Second, we present an email prioritization approach. Third, we introduce a new strategy for deals recommendation via email. All three methods improved the results of state-of-the-art algorithms for the tasks being addressed, with gains in precision varying from 7% to 21%, while reducing the number of emails sent in 40% without affecting the number of customers clicking the deals in emails. | pt_BR |
dc.language | Português | pt_BR |
dc.publisher | Universidade Federal de Minas Gerais | pt_BR |
dc.publisher.initials | UFMG | pt_BR |
dc.rights | Acesso Aberto | pt_BR |
dc.subject | Recommender Systems | pt_BR |
dc.subject | Daily-deals sites | pt_BR |
dc.subject.other | Sistema de recomendação | pt_BR |
dc.subject.other | Computação | pt_BR |
dc.subject.other | Sistemas de recuperação de informação | pt_BR |
dc.title | Revenue optimization and customer targeting in daily-deals sites | pt_BR |
dc.type | Tese de Doutorado | pt_BR |
Appears in Collections: | Teses de Doutorado |
Files in This Item:
File | Description | Size | Format | |
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an_siomendeslacerda.pdf | 1.77 MB | Adobe PDF | View/Open |
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