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Learning to rank : improving the performance of an entropy-driven advisory system using implicit user feedback

Kingma, S. (2008) Learning to rank : improving the performance of an entropy-driven advisory system using implicit user feedback. Master's Thesis / Essay, Artificial Intelligence.

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Koga-Miyata is a Dutch bicycle manufacturer specializing in the design and production of bicycles intended for the high-end segment. Yearly Koga markets about 60 to 70 different bicycles segmented into 6 to 8 partly overlapping segments. At times this overlap makes it relatively difficult for consumers to determine which Koga bicycles fit their needs best. To support their consumers Koga launched an online bicycle advisory system on their website on By answering a number of simple use targeted questions, visitors of the Koga website can use this system to retrieve a list of possibly appropriate bicycles. The advisory system was built using a number of techniques from the field of decision tree learning and information theory specifically. Borrowing from these concepts it proved to be possible to design a system in which the order of the questions is not provided beforehand. Given the answers provided by a user of the system to a series of questions, the system automatically selects the next best question to put to the user. In contrast to most other advisory systems where the order of the questions is programmed into the system, maintenance of this system is much easier. Changes do not need to be programmed, but can be configured using a simple maintenance tool. Various learning methods from the AI field of machine learning are explored for their usefulness in this context, the most promising being artificial neural networks (ANN) and Bayesian learning.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:29
Last Modified: 15 Feb 2018 07:29

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