Learning Probabilistic User Models

Abstract

We describe two applications that use rated text documents to induce a model of the user's interests. Based on our experiments with these applications we propose the use of a probabilistic learning algorithm, the Simple Bayesian Classifier (SBC), for user modeling tasks. We discuss the advantages and disadvantages of the SBC and present a novel extension to this algorithm that is specifically geared towards improving predictive accuracy for datasets typically encountered in user modeling and information filtering tasks. Results from an empirical study demonstrate the effectiveness of our approach. 1. Introduction The acquisition of user models for interactive computer systems has been addressed in many different ways. Most approaches discussed in the user modeling literature (e.g. work reported in Kobsa, Wahlster 1989) are based on predefined knowledge about users or groups of users, e.g. in form of stereotypes or inference rules. For tasks that require a detailed model about the use...