This can be accomplished by transforming the iešs

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Industrial Maintenance
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Chapter 22 / Exercise 3
Industrial Maintenance
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mendation. This can be accomplished by transforming the IEŠs mathematical equations into linguisticrules more easily understood by the human.A nice feature of the fuzzy-neural architecture used in the IE module is that it can be expressed bya set of fuzzy IF/THEN inference rules and these rules can be easily extracted automatically [33]. TheJE takes the set of IF/THEN rules from the IE and the set of recommended configurations from the PSEand develops a rationale for selecting each product. The value of the JE is that it creates confidence inthe recommendation.5Preliminary testsRubicon is implemented using a complimentary modular software approach that encapsulates theindividual computational blocks, as well as the necessary software architecture emphasizing a stable andreusable model that is compliant with the J2EE technology standard.Although still under development, Rubicon was sufficiently developed to be submitted to usabilitytesting by two major PC manufacturers. Each test involved about a dozen users and compared three RS.
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Chapter 22 / Exercise 3
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14R˘azvan Andonie, J. Edward Russo, Rishi DeanOne was the manufacturer’s current online RS, one was an attractive competitor, while the third wasRubicon. The results made available revealed that Rubicon was judged clearly superior in both tests. Forinstance, in one test, when asked which of the three RS the user would “be most likely to use again",nine of eleven respondents chose Rubicon.Rubicon was tested online by a webhosting services provider. Of 2200 online users who began aconversation, 83% completed it to the point of receiving a recommendation (which was the only resultmade available to us). This was judged by the host company to be an extraordinary high completion (i.e.,non-abandonment) rate.6ConclusionsWe have built Rubicon to meet the criteria described in Section 3.We have used principles andtechniques from artificial intelligence and behavioral sciences. Since we have focused on the core system,other modules of Rubicon, used for prediction, customer profiling, and marketing segmentation wereomitted. It was a challenging task to build Rubicon, especially because of its generic character. Makingthe system largely independent of a specific e-commerce application required greater complexity andabstraction.But do we really need a generic RS? From a user perspective this may be a non-issue.However, for the RS designer and software engineer this is a critical requirement. We should think notonly in terms of how to use a RS, but also how to build it and how to adapt it fast for very differentapplication areas.References[1]I. Vollrath, W. Wilke, and R. Bergmann, Case-based reasoning support for online catalog sales,IEEEInternet Computing, July-August, pp. 47-54, 1998.[2]R. Andonie, J. E. Russo, and R. Dean, Crossing the rubicon for an intelligent advisor,Proceedingsof the Wokshop Beyond Personalization 2005, in conjunction with the International Conference onIntelligent User Interfaces IUI’05, San Diego, CA, pp. 7-12, 2005.[3]R. Batra and O. T. Ahtola, Measuring the hedonic and utilitarian sources of consumer attitudes,

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