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Tech Report kmi-00-06 Abstract


Profiling your Customers using Bayesian Networks
Techreport ID: kmi-00-06
Date: 2000
Author(s): Paola Sebastiani, Marco Ramoni and Alexander Crea
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This report describes a complete Knowledge Discovery session using Bayesware Discoverer, a program for the induction of Bayesian networks from incomplete data. We build two causal models to help an American Charitable Organization understand the characteristics of respondents to direct mail fund raising campaigns. The first model is a Bayesian network induced from the database of 96,376 Lapsed donors to the June '97 renewal mailing. The network describes the dependency of the probability of response to the renewal mail on a subset of the variables in the database. The second model is a Bayesian network representing the dependency of the dollar amount of the gift on the variables in the same reduced database. This model is induced from the 5% of cases in the database corresponding to the respondents to the renewal campaign. The two models are used for both predicting the expected gift of a donor and understanding the characteristics of donors. These two uses can help the charitable organization to maximize the profit.

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Also in ACM SIGKDD Explorations, 1(2), 2000
 
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Social Software is...


Social Software
Social Software can be thought of as "software which extends, or derives added value from, human social behaviour - message boards, musical taste-sharing, photo-sharing, instant messaging, mailing lists, social networking."

Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.