Information Filtering Using Kullback-Leibler Divergence
In this paper we describe an information filtering system using the Kullback-Leibler divergence. To cope with information flood, many information filtering systems have been proposed up to now. Since almost all information filtering systems are developed with techniques of information retrieval, machine learning, and pattern recognition, they often use a linear function as the discriminant function. To classify information in the field of document classification more precisely, the systems have been reported which use a non-linear function as the discriminant function. The proposed method is to use the Kullback-Leibler divergence as the discriminat function which denotes to user's interest in the information filtering system. To identify the optimal discriminat function with documents which a user evaluates, we decide the optimal function using the genetic algorithm. We compare the present method with the other one using a linear discriminant function and confirm the effectiveness of the proposed method.