evaluating adaptive user profiles for news classification
Never before have so many information sources been available. Most are accessible on-line and some exist on the Internet alone. However, this large information quantity makes interesting articles hard to find. Modern Personal Digital Assistants (PDAs), mobile phones, and the advent of ubiquitous computing will further complicate matters. Away from the desktop, the time to select important articles might be even harder to find. Strategies to select relevant information are sorely needed.One such strategy is content-based filtering, coupled with User Profiles. Our prototype uses a Bayesian classifier to select articles of interest to a specific user, according to his profile. The articles are extracted from web pages and displayed in a zoomable interface-based browser on a PDA. Interests may change over time, making it important to keep the profile up to date. The system monitors the users' reading behaviors, from which it infers their interest in particular articles and updates the profile accordingly. Results show that, from the start, most articles are correctly classified. An initial profile opposite to the user's actual interests can be reversed in less than ten days, showing the robustness of our approach. A user's interest in an article is inferred with a high degree of accuracy (over 90\%).
user profiles content-based filter bayesian classifier mobile and ubiquitous computing