Personal information agents have emerged in the last decade to help users to cope with the increasing amount of information available on the Internet. These agents are intelligent assistants that perform several information-related tasks such as finding, filtering and monitoring relevant information on behalf of users or communities of users. In order to provide personalized assistance, personal agents rely on representations of user information interests and preferences contained in user profiles. In this paper, we present a summary of the state-of-the-art in user profiling in the context of intelligent information agents. Existing approaches and lines of research in the main dimensions of user profiling, such as acquisition, learning, adaptation and evaluation, are discussed.

The terminology of Machine Learning and Data Mining methods does not always allow a simple match between practical problems and methods. While some problems look similar from the user's point of view, but require different methods to be solved, some others look very different, yet they can be solved by applying the same methods and tools. Choosing appropriate Machine Learning methods for problem solving in practice is therefore largely a matter of experience and it is not realistic to expect a simple look-up table with matches between problems and methods. However, some guidelines can be given and a collection that summarizes other people's experience can also be helpful. A small number of definitions characterize the tasks that are performed by a large proportion of methods. Most of the variation in methods is concerned with differences in data types and algorithmic aspects of methods. In this paper, we summarize the main task types and illustrate how a wide variety of practical problems are formulated in terms of these tasks. The match between problems and tasks is illustrated with a collection of example applications with the aim of helping to express new practical problems as Machine Learning tasks. Some tasks can be decomposed into subtasks, allowing a wider variety of matches between practical problems and (combinations of) methods. We review the main principles for choosing between alternatives and illustrate this with a large collection of applications. We believe that this provides some guidelines.