Preprocessing uncertain user profile data: Inferring user's actual age from ages of the user's neighbors

Sung Hyuk Park, Sang Pil Han, Soon Young Huh, Hojin Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

User profile data (for example, age and sex) is usually self-reported by users, so it is prone to human errors or biases. For example, a user can be reluctant to provide a company with private information such as his/her actual age upon subscription, thus the user either does not fill in the age column or put in some random numbers to avoid unwanted privacy intrusion. However, inaccurate or uncertain user profile data undermines the integrity of a company's marketing or operational intelligence. Targeting customers based on uncertain user profile data will not as effective as targeting customers based on accurate user profile data. Thus companies perform preprocessing on user profile data as part of effort to maintain the accuracy of their user profile data. This paper presents a study of preprocessing uncertain user profile data based on a proposed simple collaborative learning algorithm. We demonstrate that a user's accurate profile information can be inferred from profile information of the user's social network neighbors. Particularly, we address the issue of how a communication service company can verify whether a user's reported age is true or not. We implement a simple collaborative learning algorithm using mobile network data. The dataset contains anonymized user data from a large Korean mobile company, capturing 174,071 users' demographic profiles and their communication histories. To construct a mobile social network among users, we collect 3G voice call histories including 561,787 unique call receivers who belong to the same service carrier. Results reveal that the prediction accuracy of the proposed method based on voice network data is 97% which is very high compared to 53%, the best accuracy by among competing methods and indicates that our method effectively detects users with great discrepancy between self-reported age and actual age.

Original languageEnglish (US)
Title of host publicationProceedings - 25th IEEE International Conference on Data Engineering, ICDE 2009
Pages1619-1624
Number of pages6
DOIs
StatePublished - Jul 8 2009
Externally publishedYes
Event25th IEEE International Conference on Data Engineering, ICDE 2009 - Shanghai, China
Duration: Mar 29 2009Apr 2 2009

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other25th IEEE International Conference on Data Engineering, ICDE 2009
CountryChina
CityShanghai
Period3/29/094/2/09

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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