IJCAI-97 TUTORIALS

Machine Learning and Information Filtering on the Internet

To perform such tasks, a profile of the user's interests must be created. In this tutorial, we will focus on the learning and representation of user profiles, the methods for collecting user feedback, and the representation of information sources. This tutorial will review a variety the findings from several decades of research on information retrieval focusing on approaches to information filtering and classification. Next, machine learning approaches to classification will be described including decision trees, nearest neighbor algorithms, Bayesian classifiers and neural networks. We will discuss how they may be used to learn user profiles. The relationship between machine learning and classic approaches from information retrieval will be discussed. Finally, recent developments such as collaborative filtering, efficient rule learners, combining multiple models, weighted majority algorithms and infinite attribute models will be described.

The technology will be illustrated with examples from a variety of information agents including LIRA, NewsWeeder, WebWatcher, WebDoggie, InfoFinder, Inquery, Letizia, firefly, InfoFinder, Syskill & Webert, DICA and the Remembrance Agent.

Prerequisite Knowledge