Article details
Title: Crossing the Rubicon: A Generic Intelligent Advisor
Author(s):  Răzvan Andonie;  J. Edward Russo;  Rishi Dean;  
Keywords:  Recommender systems, electronic commerce, user interface, user modeling



CITE THIS PAPER AS:
Andonie R., Russo J.E., Dean R., Crossing the Rubicon: A Generic Intelligent Advisor , International Journal of Computers Communications & Control, ISSN 1841-9836, 2(1):5-16, 2007.
Abstract:  
Recommender systems (RS) are being used by an increasing number of e-commerce sites to help consumers find the personally best products. We define here the criteria that a RS should satisfy, drawing on concepts from behavioral science, computational intelligence, and data mining. We present our conclusions from building the WiseUncle RS and give its general description. Rather than being an advisor for a particular application, WiseUncle is a generic RS, a platform for generating application-specific advisors.
Introduction:  
E-commerce sites use RS to guide visitors through the buying process by providing customized information and product recommendations. Some actual online recommender systems are described in [26, 31, 32]. Several well-known e-commerce businesses use, or have used, RS technology on their web sites: Amazon, Travelocity, BMW, MovieFinder, and Dell among them. Although commercial RS use began several years ago, we are still only beginning to use such systems on a large scale. Overviews of the relatively short history of RS and the techniques used may be found in [16, 30].
What is an “intelligent RS"? We will consider as intelligence the use of artificial intelligence features, such as adaptation, integration of learning algorithms, explanation, and case-based planning. Such an intelligent product search engine for online catalog sales is Analog Devices [1], developed at University of Kaiserslautern.
We begin by specifying the performance goals of a RS. This delineation of what a successful RS needs to be able to do leads to an analysis of the criteria that a successful RS must meet. Then we describe our own RS, namedWiseUncle. The paper concludes with the results of the preliminary tests of WiseUncle performed by three e-commerce businesses. A preliminary paper describing our results may be found in [2].
Conclusions:  
We have built Rubicon to meet the criteria described in Section 3. We have used principles and techniques 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 were omitted. It was a challenging task to build Rubicon, especially because of its generic character. Making the system largely independent of a specific e-commerce application required greater complexity and abstraction. 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 not only in terms of how to use a RS, but also how to build it and how to adapt it fast for very different application areas.
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