Evaluation and Validation of Two Approaches to User Profiling

In the Internet era, huge amounts of data are available to everybody, in every place and at any moment. Searching for relevant information can be overwhelming, thus contributing to the user's sense of information overload. Building systems for assisting users in this task is often complicated by the difficulty in articulating user interests in a structured form - a profile - to be used for searching. Machine learning methods offer a promising approach to solve this problem. Our research focuses on supervised methods for learning user profiles which are predictively accurate and comprehensible. The main goal of this paper is the comparison of two different approaches for inducing user profiles, respectively based on Inductive Logic Programming (ILP) and probabilistic methods. An experimental session has been carried out to compare the effectiveness of these methods in terms of classification accuracy, learning and classification time, when coping with the task of learning profiles from textual book descriptions rated by real users according to their tastes.