Accelerated learning of user profiles

Name: Accelerated learning of user profiles
Author: Atahan, Pelin, Sarkar, S.

İsim	Accelerated learning of user profiles
Yazar	Atahan, Pelin, Sarkar, S.
Basım Tarihi:	2011-02
Basım Yeri	- Informs
Konu	Personalization, Bayesian learning, Information theory, Recommendation systems
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0025-1909
Kayıt Numarası	760c1799-7e1d-4934-8fad-9a79ec243ae8
Lokasyon	Sectoral Education and Professional Development
Tarih	2011-02
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation.
DOI	10.1287/mnsc.1100.1266
Cilt	57

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Accelerated learning of user profiles

Yazar Atahan, Pelin, Sarkar, S.

Basım Tarihi 2011-02

Basım Yeri - Informs

Konu Personalization, Bayesian learning, Information theory, Recommendation systems

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0025-1909

Kayıt Numarası 760c1799-7e1d-4934-8fad-9a79ec243ae8

Lokasyon Sectoral Education and Professional Development

Tarih 2011-02

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation.

DOI 10.1287/mnsc.1100.1266

Cilt 57