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Titolo:
Expert-driven validation of rule-based user models in personalization applications
Autore:
Adomavicius, G; Tuzhilin, A;
Indirizzi:
NYU, Courant Inst Math Sci, Dept Comp Sci, New York, NY 10012 USA NYU NewYork NY USA 10012 Math Sci, Dept Comp Sci, New York, NY 10012 USA NYU, Stern Sch Business, Dept Informat Syst, New York, NY 10012 USA NYU New York NY USA 10012 ess, Dept Informat Syst, New York, NY 10012 USA
Titolo Testata:
DATA MINING AND KNOWLEDGE DISCOVERY
fascicolo: 1-2, volume: 5, anno: 2001,
pagine: 33 - 58
SICI:
1384-5810(200101/04)5:1-2<33:EVORUM>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Soggetto:
ASSOCIATION RULES; DISCOVERY;
Keywords:
personalization; profiling; rule discovery; post-analysis; validation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
61
Recensione:
Indirizzi per estratti:
Indirizzo: Adomavicius, G NYU, Courant Inst Math Sci, Dept Comp Sci, 251 Mercer St, New York, NY 10012 USA NYU 251 Mercer St New York NY USA 10012 York, NY 10012 USA
Citazione:
G. Adomavicius e A. Tuzhilin, "Expert-driven validation of rule-based user models in personalization applications", DATA M K D, 5(1-2), 2001, pp. 33-58

Abstract

In many e-commerce applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, it is important to build personalized profiles of individual usersfrom their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned fromuser transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate user profiles by separating "good" rules from the "bad. " This validation process should be done with an explicit participation of the human expert. However, complications may arise because there can be very large numbers of rules discovered in the applications that deal with many users, and the expert cannot perform the validation on a rule-by-rule basis ina reasonable period of time. This paper presents a framework for building behavioral profiles of individual users. It also introduces a new approach to expert-driven validation of a very large number of rules pertaining to these users. In particular, it presents several types of validation operators, including rule grouping, filtering, browsing, and redundant rule elimination operators, that allow a human expert validate many individual rules ata time. By iteratively applying such operators, the human expert can validate a significant part of all the initially discovered rules in an acceptable time period. These validation operators were implemented as a part of a one-to-one profiling system. The paper also presents a case study of using this system for validating individual user rules discovered in a marketing application.

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Documento generato il 05/04/20 alle ore 23:10:28