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A recommender system for e-retail
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 pro...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2017
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| _version_ | 1867613201504403456 |
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| access_status_str | Open Access |
| author | Walwyn, Thomas |
| author2 | Varughese, Melvin |
| author_browse | Varughese, Melvin Walwyn, Thomas |
| author_facet | Varughese, Melvin Walwyn, Thomas |
| author_sort | Walwyn, Thomas |
| collection | Thesis |
| description | 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). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/22889 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:21.936Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/22889 A recommender system for e-retail Walwyn, Thomas Varughese, Melvin Statistical Sciences Advanced Analytics And Decision Sciences 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). 2017-01-23T07:46:24Z 2017-01-23T07:46:24Z 2016 Master Thesis Masters MSc http://hdl.handle.net/11427/22889 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Statistical Sciences Advanced Analytics And Decision Sciences Walwyn, Thomas A recommender system for e-retail |
| thesis_degree_str | Master's |
| title | A recommender system for e-retail |
| title_full | A recommender system for e-retail |
| title_fullStr | A recommender system for e-retail |
| title_full_unstemmed | A recommender system for e-retail |
| title_short | A recommender system for e-retail |
| title_sort | recommender system for e retail |
| topic | Statistical Sciences Advanced Analytics And Decision Sciences |
| url | http://hdl.handle.net/11427/22889 |
| work_keys_str_mv | AT walwynthomas arecommendersystemforeretail AT walwynthomas recommendersystemforeretail |