Auflistung nach Autor:in "Faust, Oliver"

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A genetic algorithm for optimizing parameters for ant colony optimization solving capacitated vehicle routing problems
(ACM, 2020) Faust, Oliver; Mehli, Carlo; Hanne, Thomas; Dornberger, Rolf
04B - Beitrag Konferenzschrift
An Automatic Case Acquisition Approach
(Hochschule für Wirtschaft FHNW, 2020) Faust, Oliver; Hinkelmann, Knut
As customer satisfaction can be considered as a competitive advantage, technical helpdesk agents have to be able to solve customer’s problems fast and hence need to be very knowledgeable in their particular domain. A Case-Based Reasoning (CBR) system can support them by providing a case base of past problem’s solutions. Building and maintaining a case base, however, is a tedious and costly task. In order to have a good quality, human review is required which can quickly become a bottleneck. How to bestbuild case bases from raw data respectively reuse and leverage hidden knowledge in existing datasets is therefore an ongoing research topic. This study aims to build a case base from a helpdesk ticket dataset in an automatic way, without having a human task bottleneck. As helpdesk tickets are not intended to perform CBR with, the raw data is usually very messy and not properly labelled. Some tickets however can contain valuable troubleshooting information....
11 - Studentische Arbeit
Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation
(2019) Hinkelmann, Knut; Blaser, Monika; Faust, Oliver; Horst, Alexander; Mehli, Carlo; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank; Clark, Peter
This research is about combination of data-driven and knowledge-based recommendations The research is made in an application scenario for whisky recommendation, where a guest chats with a recommender system. Preferences about taste are difficult to express and the knowledge about taste is tacit and thus can hardly be represented and used adequately. People or not aware of how to describe flavors in a standardized way and how to do a justified choice. This is because knowledge about taste is mainly tacit knowledge. To deal with this knowledge, data-driven recommendation is adequate. On the other hand, in particular experienced customers use knowledge about distilleries, locations and the distillery process to express their preferences and want to have arguments for the recommended products. This shows that a combination of data-driven and knowledge-based recommendations is appropriate in areas where tacit knowledge and explicit knowledge are available.
04B - Beitrag Konferenzschrift