Construction of a classifier using AdaBoost for information filtering

We describe an information filtering system using AdaBoost. To realize the filtering system, we created a user profile which presents the user's interests. Since the user's interests are complex, the user profile becomes a nonlinear discriminant function. However, it is difficult to decide on an appropriate discriminant function. We used AdaBoost to modify the appropriate user profile. AdaBoost is an ensemble algorithm which combines weak learners and improves the accuracy of a classifier. In this method, the weak learners for AdaBoost is a linear discriminant function which is created with a genetic algorithm. We carried out experiments for an information filtering service on an NTCIR2 test collection, and we discuss the effectiveness of the method.