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E-commerce platform designed for continuous optimization and personalization
Dräxler, Peter ; Peška, Ladislav (advisor) ; Bernhauer, David (referee)
The ability of an e-commerce company to collect, synthesize, and utilize data can be vital to its survival. The degree to which a company has the ability to gather data about sources of conversion is proportional to their ability to allocate advertising / im- plementation budget effectively. Measurement and optimization of user flow on a website is equally important as it can deliver a measurable increase in user loyalty, revenue, and profits. Similarly, it can reveal that numerous improvements, while appearing effective on paper, may not perform as well in real-world scenarios. This thesis focuses on the construction of an e-commerce platform centered around data collection and analysis. We use the data to conduct continuous, real-time experi- ments on the platform using the contextual multi-armed bandits algorithm. We develop a recommendation system based on collaborative filtering and set up an experiment to evaluate it's real-world performance. The platform can be easily connected to a business intelligence dashboard to allow data exploration, to support its management in making informed tactical & strategic decisions. The platform is developed with Blazor, which is an emerging technology that en- ables usage on C# code in the browser with compilation to WebAssembly as opposed to JavaScript. We describe...
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