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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 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).

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/22889
Date January 2016
CreatorsWalwyn, Thomas
ContributorsVarughese, Melvin
PublisherUniversity of Cape Town, Faculty of Science, Department of Statistical Sciences
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeMaster Thesis, Masters, MSc
Formatapplication/pdf

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