Back to Search

Accelerated learning of user profiles

Title	Accelerated learning of user profiles
Author	Atahan, Pelin, Sarkar, S.
Publication Date:	2011-02
Publication Place	- Informs
Subject	Personalization, Bayesian learning, Information theory, Recommendation systems
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0025-1909
Record ID	760c1799-7e1d-4934-8fad-9a79ec243ae8
Library Location	Sectoral Education and Professional Development
Date	2011-02
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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

View in source Özyeğin University Özyeğin Üniversitesi

Özyeğin University

Accelerated learning of user profiles

Author Atahan, Pelin, Sarkar, S.

Publication Date 2011-02

Publication Place - Informs

Subject Personalization, Bayesian learning, Information theory, Recommendation systems

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0025-1909

Record ID 760c1799-7e1d-4934-8fad-9a79ec243ae8

Library Location Sectoral Education and Professional Development

Date 2011-02

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

Sample Text 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