A recommender system for e-retail

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dc.contributor.advisorVarughese, Melvinen_ZA
dc.contributor.authorWalwyn, Thomasen_ZA
dc.date.accessioned2017-01-23T07:46:24Z
dc.date.available2017-01-23T07:46:24Z
dc.date.issued2016en_ZA
dc.description.abstractThe e-retail sector in South Africa has a significant opportunity to capture a large portion of the country's retail industry. Central to seizing this opportunity is leveraging the advantages that the online setting affords. In particular, the e-retailer can offer an extremely large catalogue of products; far beyond what a traditional retailer is capable of supporting. However, as the catalogue grows, it becomes increasingly difficult for a customer to efficiently discover desirable products. As a consequence, it is important for the e-retailer to develop tools that automatically explore the catalogue for the customer. In this dissertation, we develop a recommender system (RS), whose purpose is to provide suggestions for products that are most likely of interest to a particular customer. There are two primary contributions of this dissertation. First, we describe a set of six characteristics that all effective RS's should possess, namely; accuracy, responsiveness, durability, scalability, model management, and extensibility. Second, we develop an RS that is capable of serving recommendations in an actual e-retail environment. The design of the RS is an attempt to embody the characteristics mentioned above. In addition, to show how the RS supports model selection, we present a proof-of-concept experiment comparing two popular methods for generating recommendations that we implement for this dissertation, namely, implicit matrix factorisation (IMF) and Bayesian personalised ranking (BPR).en_ZA
dc.identifier.apacitationWalwyn, T. (2016). <i>A recommender system for e-retail</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/22889en_ZA
dc.identifier.chicagocitationWalwyn, Thomas. <i>"A recommender system for e-retail."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2016. http://hdl.handle.net/11427/22889en_ZA
dc.identifier.citationWalwyn, T. 2016. A recommender system for e-retail. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Walwyn, Thomas AB - The e-retail sector in South Africa has a significant opportunity to capture a large portion of the country's retail industry. Central to seizing this opportunity is leveraging the advantages that the online setting affords. In particular, the e-retailer can offer an extremely large catalogue of products; far beyond what a traditional retailer is capable of supporting. However, as the catalogue grows, it becomes increasingly difficult for a customer to efficiently discover desirable products. As a consequence, it is important for the e-retailer to develop tools that automatically explore the catalogue for the customer. In this dissertation, we develop a recommender system (RS), whose purpose is to provide suggestions for products that are most likely of interest to a particular customer. There are two primary contributions of this dissertation. First, we describe a set of six characteristics that all effective RS's should possess, namely; accuracy, responsiveness, durability, scalability, model management, and extensibility. Second, we develop an RS that is capable of serving recommendations in an actual e-retail environment. The design of the RS is an attempt to embody the characteristics mentioned above. In addition, to show how the RS supports model selection, we present a proof-of-concept experiment comparing two popular methods for generating recommendations that we implement for this dissertation, namely, implicit matrix factorisation (IMF) and Bayesian personalised ranking (BPR). DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - A recommender system for e-retail TI - A recommender system for e-retail UR - http://hdl.handle.net/11427/22889 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/22889
dc.identifier.vancouvercitationWalwyn T. A recommender system for e-retail. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/22889en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherStatistical Sciencesen_ZA
dc.subject.otherAdvanced Analytics And Decision Sciencesen_ZA
dc.titleA recommender system for e-retailen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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