Exploring digital collections to find information relevant to a user's interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users' interests are maintained. This article presents a new method, based on the classic Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalog of e-commerce Web sites. Experiments have been carried out on several data sets, and results have been compared with those obtained using an inductive logic programming (ILP) approach and a probabilistic one.
Text Learning for User Profiling in e-Commerce
DEGEMMIS, MARCO;LOPS, PASQUALE;FERILLI, Stefano;DI MAURO, NICOLA;BASILE, TERESA MARIA;SEMERARO, Giovanni
2006-01-01
Abstract
Exploring digital collections to find information relevant to a user's interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users' interests are maintained. This article presents a new method, based on the classic Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalog of e-commerce Web sites. Experiments have been carried out on several data sets, and results have been compared with those obtained using an inductive logic programming (ILP) approach and a probabilistic one.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
