This article presents a comprehensive overview of methods and techniques used for the evaluation of user-adaptive systems. It describes the methodologies derived both from the evaluation of human–computer interaction systems and from information retrieval and information filtering systems by giving examples of the application of these methodologies in the user-adaptive systems. The state of the art and the main results in the evaluation of these systems are reported. In particular, empirical evaluation and layered approaches are discussed in detail. Finally, focus on less explored methodologies, such as qualitative approaches (e.g. Grounded Theory), is proposed.

It is a well-known fact that the data mining process can generate many hundreds and often thousands of patterns from data. The task for the data miner then becomes one of determining the most useful patterns from those that are trivial or are already well known to the organization. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual worth. This article presents a review of the available literature on the various measures devised for evaluating and ranking the discovered patterns produced by the data mining process. These so-called interestingness measures are generally divided into two categories: objective measures based on the statistical strengths or properties of the discovered patterns and subjective measures that are derived from the user's beliefs or expectations of their particular problem domain. We evaluate the strengths and weaknesses of the various interestingness measures with respect to the level of user integration within the discovery process.

In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory.