In the recent years, the astonishing growth of the Internet and the considerable advances of Web technologies have promoted the development of electronic commerce. While e-commerce has not necessarily allowed businesses to produce more products, it has allowed them to provide consumers with more choices. Instead of tens of thousands of books in a superstore, consumers may choose among millions of books in an online store. Increasing choice has also increased the amount of information that scrupulous customers want process before they are able to select which items meet their needs. One way to address this information overload is the use of personalized systems able to support customers in retrieving information about products they are really interested in. Personalization has become an important strategy in Business-to-Consumer electronic commerce, where a user explicitly wants the e-commerce site to consider his or her own information, such as preferences, in order to improve access to relevant product information. In this paper, we propose a scheme to learn user profiles to support Internet customers. The proposed scheme is designed to handle different levels of users' interests simultaneously. Experimental evaluations show the promise of the approach.

User Profiling to Support Internet Customers: what do you want to buy today?

SEMERARO, Giovanni;DEGEMMIS, MARCO;LOPS, PASQUALE
2002-01-01

Abstract

In the recent years, the astonishing growth of the Internet and the considerable advances of Web technologies have promoted the development of electronic commerce. While e-commerce has not necessarily allowed businesses to produce more products, it has allowed them to provide consumers with more choices. Instead of tens of thousands of books in a superstore, consumers may choose among millions of books in an online store. Increasing choice has also increased the amount of information that scrupulous customers want process before they are able to select which items meet their needs. One way to address this information overload is the use of personalized systems able to support customers in retrieving information about products they are really interested in. Personalization has become an important strategy in Business-to-Consumer electronic commerce, where a user explicitly wants the e-commerce site to consider his or her own information, such as preferences, in order to improve access to relevant product information. In this paper, we propose a scheme to learn user profiles to support Internet customers. The proposed scheme is designed to handle different levels of users' interests simultaneously. Experimental evaluations show the promise of the approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/121276
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