The Computer Journal Advance Access originally published online on January 4, 2008
The Computer Journal 2009 52(7):771-788; doi:10.1093/comjnl/bxm107
| ||||||||||||||||||||||||||||||||||||||||||||||||
This article appears in the following The Computer Journal issue: Incorporating Profiling Expertise and Behaviour Special Issue [View the issue table of contents]
Interest Drifts in User Profiling: A Relevance-Based Approach and Analysis of Scenarios
1 ISISTAN Research Institute, Facultad de Ciencias Exactas, UNCPBA Campus Universitario, CP B7001BBO, Tandil, Buenos Aires, Argentina
2 CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
* Correspending author: dgodoy{at}exa.unicen.edu.ar
Received 19 February 2007; revised 9 November 2007
For personal information agents, user profiles have to represent user interests and preferences in order to satisfy long-term information needs. An implicit assumption in user-profiling is the existence of persistent interests which, however, might suffer some changes over time. Each time the interests of a user change, his profile becomes inaccurate and the predictive quality decreases. Adaptation of user profiles is, therefore, an essential requirement for personal agents that need to be capable of adjusting their behavior quickly in order to shorten the period of reduced predictive quality. In this paper, a user-profiling technique named WebProfiler, which learns a hierarchical representation of user interests using conceptual clustering, is augmented with an adaptation strategy based on relevance feedback and time-based forgetting in order to deal with drifting interests. We empirically evaluate the performance of this strategy by analyzing its behavior on multiple scenarios of interest drifts and shifts.
Key Words: User profiling personal agents interest drifts