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Titlebook: Applications of Data Mining to Electronic Commerce; Ron Kohavi,Foster Provost Book 2001 Springer Science+Business Media New York 2001 Elec

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https://doi.org/10.1007/978-3-662-66015-7have been around for decades, without moving significantly beyond the domain of computer scientists, statisticians, and hard-core business analysts. Why are electronic commerce systems any different from other data—mining applications?
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https://doi.org/10.1007/978-3-662-66015-7ting environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers
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https://doi.org/10.1007/978-3-662-66015-7tomers, it is important to build personalized profiles of individual users from their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since ma
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https://doi.org/10.1007/978-3-031-20698-6to a serious business tool. Recommender systems use product knowledge—either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers—to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an
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his fast moving area. ..Applications of Data Mining to Electronic Commerce. .servesas an excellent reference, providing insight into some of the mostchallenging research issues in the field.978-1-4613-5648-6978-1-4615-1627-9
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Book 2001.Applications of Data Mining to Electronic Commerce. bringstogether in one place important contributions and up-to-date researchresults in this fast moving area. ..Applications of Data Mining to Electronic Commerce. .servesas an excellent reference, providing insight into some of the mostchallenging research issues in the field.
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